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Pipeline run

1251e53c-b467-4dbc-b428-839243f1b2fc

Pipeline LLM cost (USD)
API 1: $0.0032 API 2: $0.0002 API 3: $0.0000 Total: $0.0034

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Data pipeline development
Design, build, and maintain Snowflake/PLSQL data pipelines and ETL flows, while checking data quality, supporting data modeling, and documenting data dictionaries and technical processes for cross-team needs.
""Create data pipelines and ensure data quality by implementing ETL (extract, transform and load) processes to migrate and deploy data across systems.""
Tech stack maturity
Modern Cloud Native cache hit
Snowflake is a cloud-native data platform, and a Data Engineer role centered on it aligns with modern cloud-native stack maturity.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (7)
Snowflake ETL Data Modeling Data Quality PL/SQL Data Dictionary Data Governance
Skill cluster (2 dimension groups, role-scoped)
Cloud Data Warehouses
Snowflake
Cross-cutting / unaligned
ETL Data Modeling Data Quality PL/SQL Data Dictionary Data Governance
Show KRA description ↓
As a Data Engineer, you will be responsible for designing, developing, and maintaining data solutions for data generation, collection, and processing. Your typical day will involve creating data pipelines, ensuring data quality, and implementing ETL processes to migrate and deploy data across systems using Snowflake Data Warehouse and Oracle Procedural Language Extensions to SQL (PLSQL). - Design, develop, and maintain data solutions for data generation, collection, and processing using Snowflake Data Warehouse and Oracle Procedural Language Extensions to SQL (PLSQL). - Create data pipelines and ensure data quality by implementing ETL (extract, transform and load) processes to migrate and deploy data across systems. - Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs. - Develop and maintain technical documentation for data solutions, including data models, data dictionaries, and ETL processes. - Must To Have Skills: Experience with Snowflake Data Warehouse. - Good To Have Skills: Experience with Oracle Procedural Language Extensions to SQL (PLSQL). - Strong understanding of ETL (extract, transform and load) processes. - Experience with data modeling and data dictionary development. - Experience with data quality and data governance best practices.

Signals

Skill data-engineer
0.25
Alias data-engineer
1.00
KRA data-engineer
0.65

Post-classification

Centroidupdated · n=143
Alias collision log
New-role queue
New skills captured5
New KRA captured

Captured for admin review

ETL primary Data Engineer pending
Data Modeling primary Data Engineer pending
Data Dictionary Data Engineer pending
Data Quality primary Data Engineer pending
Data Governance Data Engineer pending
Status: completed Created: 2026-05-27T14:14:44.041997Z Updated: 2026-05-27T14:16:39.388109Z API 3 duration: 4875 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Engineer

CASE A

slug: data-engineer · id: 2 · source: db

Exact alias hit on data-engineer (1.0) — no other alias at this confidence; skill_top data-engineer 0.25 does not contradict

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
2
Skipped

Job description

Project Role : Data Engineer

Project Role Description : Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems.

Must have skills : Snowflake Data Warehouse

Good to have skills : Oracle Procedural Language Extensions to SQL (PLSQL)

Minimum 3 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Summary: As a Data Engineer, you will be responsible for designing, developing, and maintaining data solutions for data generation, collection, and processing. Your typical day will involve creating data pipelines, ensuring data quality, and implementing ETL processes to migrate and deploy data across systems using Snowflake Data Warehouse and Oracle Procedural Language Extensions to SQL (PLSQL). Roles & Responsibilities: - Design, develop, and maintain data solutions for data generation, collection, and processing using Snowflake Data Warehouse and Oracle Procedural Language Extensions to SQL (PLSQL). - Create data pipelines and ensure data quality by implementing ETL (extract, transform and load) processes to migrate and deploy data across systems. - Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs. - Develop and maintain technical documentation for data solutions, including data models, data dictionaries, and ETL processes. Professional & Technical Skills: - Must To Have Skills: Experience with Snowflake Data Warehouse. - Good To Have Skills: Experience with Oracle Procedural Language Extensions to SQL (PLSQL). - Strong understanding of ETL (extract, transform and load) processes. - Experience with data modeling and data dictionary development. - Experience with data quality and data governance best practices. Additional Information: - The candidate should have a minimum of 3 years of experience in Snowflake Data Warehouse. - The ideal candidate will possess a strong educational background in computer science, information technology, or a related field, along with a proven track record of delivering impactful data-driven solutions. - This position is based at our Chennai office.

15 years full time education

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Snowflake Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

ELT ETL SQL Snowpark Snowpipe Streams Tasks Time Travel VARIANT data sharing data warehouse dbt semi-structured data virtual warehouse zero-copy cloning

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Cloud Platform
Vendor
Snowflake Inc.
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Snowflake appears frequently in data/analytics job postings and is a standard cloud data warehouse platform alongside BigQuery and Redshift.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
113
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
PL/SQL Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PL/SQL id=1567 · pl-sql

Aliases — catalog

  • PL/SQL (CANONICAL)

Context tags (catalog)

Oracle PL/SQL blocks SQL*Plus bulk collect cursors data manipulation data types dynamic SQL exception handling functions packages performance tuning stored procedures transaction control triggers

Stored enrichment (catalog DB)

Category
Language
Sub-category
Procedural Sql Language
Vendor
Oracle Corporation
License
proprietary
Year introduced
1990
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: PL/SQL appears frequently in Oracle-focused job postings and remains a standard skill for Oracle database development and maintenance; it is not sunset or replaced by a newer successor.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
1173
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Modeling Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: domain modeling id=2379 · domain-modeling

Aliases — catalog

  • domain modeling (CANONICAL) primary
  • Domain Modeling (CANONICAL)

Context tags (catalog)

CQRS DDD ERD UML aggregate bounded context business logic context map context mapping data modeling domain events domain-driven design entities entity event sourcing event storming microservices repositories repository pattern service layer services value object value objects

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Domain Modeling
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in software JDs under DDD/business analysis; many roles ask for domain modeling or domain-driven design, and it remains a standard design skill rather than a niche tool.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
2831
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Application Architecture Patterns Catalog dimension db id 293

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Python Backend Developer

  • Service Architecture and Design Patterns Catalog dimension db id 18

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Ruby Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Data Dictionary Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Management
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Data Quality Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Management
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Governance Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Management
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension saved
PL/SQL in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Modeling new
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Data Modeling new
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Dictionary | type=Data Management subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Data Quality | type=Data Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Governance | type=Data Management subtype=general nature=CONCEPT lifespan=MULTI_YEAR
dimension_skill_link_proposed Data Modeling ↔ Application Architecture Patterns
dimension_skill_link_proposed Data Modeling ↔ Service Architecture and Design Patterns
nano JD Parser — gpt-4.1-nano click to toggle
RoleData Engineer
ExperienceMinimum 3 Year(s) Of Experience Is Required
DomainOther
Location Chennai, India
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "Bachelor\u0027s - Any Discipline",
      "raw": "15 years full time education",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 3,
    "raw": "Minimum 3 Year(s) Of Experience Is Required"
  },
  "job_locations": [
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      "aliases": [],
      "city": "Chennai",
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      "state": null,
      "work_mode": null
    }
  ],
  "role": "Data Engineer",
  "role_aliases": [
    "Data Engineer",
    "Data Developer",
    "ETL Developer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Summary",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "As a Data Engineer, you",
        "last_5_words": "and Oracle Procedural Language Extensions"
      },
      "text": "As a Data Engineer, you will be responsible for designing, developing, and maintaining data solutions for data generation, collection, and processing. Your typical day will involve creating data pipelines, ensuring data quality, and implementing ETL processes to migrate and deploy data across systems using Snowflake Data Warehouse and Oracle Procedural Language Extensions to SQL (PLSQL).",
      "word_count": 54
    },
    {
      "bullet_count": 4,
      "heading": "Roles \u0026 Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "- Design, develop, and maintain",
        "last_5_words": "data models, data dictionaries, and ETL processes."
      },
      "text": "- Design, develop, and maintain data solutions for data generation, collection, and processing using Snowflake Data Warehouse and Oracle Procedural Language Extensions to SQL (PLSQL).\n- Create data pipelines and ensure data quality by implementing ETL (extract, transform and load) processes to migrate and deploy data across systems.\n- Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs.\n- Develop and maintain technical documentation for data solutions, including data models, data dictionaries, and ETL processes.",
      "word_count": 81
    },
    {
      "bullet_count": 5,
      "heading": "Professional \u0026 Technical Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "- Must To Have Skills:",
        "last_5_words": "quality and data governance best practices."
      },
      "text": "- Must To Have Skills: Experience with Snowflake Data Warehouse.\n- Good To Have Skills: Experience with Oracle Procedural Language Extensions to SQL (PLSQL).\n- Strong understanding of ETL (extract, transform and load) processes.\n- Experience with data modeling and data dictionary development.\n- Experience with data quality and data governance best practices.",
      "word_count": 56
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": false,
      "skill_name": "PL/SQL"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": false,
      "skill_name": "Data Dictionary"
    },
    {
      "is_primary": true,
      "skill_name": "Data Quality"
    },
    {
      "is_primary": false,
      "skill_name": "Data Governance"
    }
  ],
  "jd_role": {
    "display_name": "Data Engineer",
    "rationale": null,
    "role_aliases": [
      "Data Engineer",
      "Data Developer",
      "ETL Developer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
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      },
      "secondary": null
    },
    "education": [
      {
        "level": "Bachelor\u0027s",
        "qualification": "Bachelor\u0027s - Any Discipline",
        "raw": "15 years full time education",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": null,
      "min": 3,
      "raw": "Minimum 3 Year(s) Of Experience Is Required"
    },
    "job_locations": [
      {
        "aliases": [],
        "city": "Chennai",
        "country": "India",
        "state": null,
        "work_mode": null
      }
    ],
    "role": "Data Engineer",
    "role_aliases": [
      "Data Engineer",
      "Data Developer",
      "ETL Developer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "Summary",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "As a Data Engineer, you",
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        },
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        "word_count": 54
      },
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        "bullet_count": 4,
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        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "- Design, develop, and maintain",
          "last_5_words": "data models, data dictionaries, and ETL processes."
        },
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          "first_5_words": "- Must To Have Skills:",
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        },
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        "word_count": 56
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    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "1251e53c-b467-4dbc-b428-839243f1b2fc",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 1.0,
        "slug": "data-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": [
          {
            "kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
            "sentence": "Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs.",
            "similarity": 0.6794
          },
          {
            "kra_text": "Maintains data catalog entries, column-level data lineage, and technical documentation to support data discoverability and governance across the organization.",
            "sentence": "Develop and maintain technical documentation for data solutions, including data models, data dictionaries, and ETL processes.",
            "similarity": 0.6407
          },
          {
            "kra_text": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
            "sentence": "Create data pipelines and ensure data quality by implementing ETL (extract, transform and load) processes to migrate and deploy data across systems.",
            "similarity": 0.6365
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.6522,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
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          },
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            "sentence": "As a Data Engineer, you will be responsible for designing, developing, and maintaining data solutions for data generation, collection, and processing.",
            "similarity": 0.4826
          },
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            "similarity": 0.4805
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        "matched_count": null,
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        "role_id": 3,
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        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
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          },
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            "similarity": 0.4911
          },
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            "similarity": 0.4571
          }
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        "matched_skills": null,
        "role_id": 15,
        "score": 0.4868,
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        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Writes runbooks, deployment guides, incident playbooks, and infrastructure documentation to support operations and knowledge sharing.",
            "sentence": "Develop and maintain technical documentation for data solutions, including data models, data dictionaries, and ETL processes.",
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          },
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          },
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          }
        ],
        "matched_count": null,
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        "role_id": 10,
        "score": 0.4614,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Create data pipelines and ensure data quality by implementing ETL (extract, transform and load) processes to migrate and deploy data across systems.",
            "similarity": 0.4791
          },
          {
            "kra_text": "Maintains ML platform runbooks, on-call escalation playbooks, and deployment procedure documentation for production operations teams.",
            "sentence": "Develop and maintain technical documentation for data solutions, including data models, data dictionaries, and ETL processes.",
            "similarity": 0.4704
          },
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Your typical day will involve creating data pipelines, ensuring data quality, and implementing ETL processes to migrate and deploy data across systems using Snowflake Data Warehouse and Oracle Procedural Language Extensions to SQL (PLSQL).",
            "similarity": 0.4067
          }
        ],
        "matched_count": null,
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        "role_id": 16,
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        "slug": "ml-ops-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Snowflake"
        ],
        "role_id": 2,
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        "total_count": 4
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
    "chosen_role": {
      "display_name": "Data Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 2,
      "score": 1.0,
      "slug": "data-engineer",
      "total_count": null
    },
    "confidence": 1.0,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [],
    "matched_kras": [],
    "matched_skills": [],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.25 does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 143,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 7842,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "ETL",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 7843,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Modeling",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 7844,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Dictionary",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 7845,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Quality",
        "status": "pending"
      },
      {
        "is_primary": false,
        "queue_id": 7846,
        "role_display_name": "Data Engineer",
        "role_slug": "data-engineer",
        "skill_name": "Data Governance",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 299,
      "existing_alias_text": "Snowflake",
      "input_term": "Snowflake",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Snowflake",
        "id": 105,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 2513,
      "existing_alias_text": "PL/SQL",
      "input_term": "PL/SQL",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "PL/SQL",
        "id": 1567,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "pl-sql",
        "sub_category_id": 1173,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 5644,
      "existing_alias_text": "Domain Modeling",
      "input_term": "Data Modeling",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "domain modeling",
        "id": 2379,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "domain-modeling",
        "sub_category_id": 2831,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
      "role_archetype": null,
      "slug": "data-engineer",
      "source": "db"
    },
    {
      "display_name": ".NET Backend Developer",
      "id": 83,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "dotnet-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Backend Developer",
      "id": 1,
      "rationale": null,
      "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
      "slug": "backend-engineer",
      "source": "db"
    },
    {
      "display_name": "Java Backend Developer",
      "id": 79,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "java-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Kotlin Backend Developer",
      "id": 84,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "kotlin-server-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Node.js Backend Developer",
      "id": 82,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "node-backend-developer",
      "source": "db"
    },
    {
      "display_name": "PHP Backend Developer",
      "id": 86,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "php-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Ruby Backend Developer",
      "id": 85,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "ruby-backend-developer",
      "source": "db"
    },
    {
      "display_name": "Scala Backend Developer",
      "id": 87,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "scala-backend-developer",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.25 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Data Warehouses",
        "id": 22,
        "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
        "slug": "cloud-data-warehouses",
        "source": "db"
      },
      "input_skill": "Snowflake",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "React Frontend Development",
        "id": 96,
        "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "PL/SQL",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Application Architecture Patterns",
        "id": 293,
        "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
        "slug": "application-architecture-patterns",
        "source": "db"
      },
      "input_skill": "Data Modeling",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": ".NET Backend Developer",
          "id": 83,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "dotnet-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Python Backend Developer",
          "id": 80,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "python-backend-developer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Service Architecture and Design Patterns",
        "id": 18,
        "rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
        "slug": "service-architecture-and-design-patterns",
        "source": "db"
      },
      "input_skill": "Data Modeling",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Developer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Java Backend Developer",
          "id": 79,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "java-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Kotlin Backend Developer",
          "id": 84,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "kotlin-server-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Node.js Backend Developer",
          "id": 82,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
        },
        {
          "display_name": "PHP Backend Developer",
          "id": 86,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "php-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Ruby Backend Developer",
          "id": 85,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "ruby-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Scala Backend Developer",
          "id": 87,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "scala-backend-developer",
          "source": "db"
        }
      ]
    }
  ],
  "input_final_skills": [
    "Snowflake",
    "PL/SQL",
    "ETL",
    "Data Modeling",
    "Data Dictionary",
    "Data Quality",
    "Data Governance"
  ],
  "input_llm_skills": [
    "Snowflake",
    "PL/SQL",
    "ETL",
    "Data Modeling",
    "Data Dictionary",
    "Data Quality",
    "Data Governance"
  ],
  "new_aliases_persisted": 0,
  "run_id": "1251e53c-b467-4dbc-b428-839243f1b2fc",
  "skills_detail": [
    {
      "aliases_in_db": [
        {
          "alias_text": "Snowflake",
          "alias_type": "CANONICAL",
          "id": 299,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Snowflake",
        "id": 105,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Data Warehouses",
            "id": 22,
            "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
            "slug": "cloud-data-warehouses",
            "source": "db"
          },
          "input_skill": "Snowflake",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Snowflake",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "PL/SQL",
          "alias_type": "CANONICAL",
          "id": 2513,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "PL/SQL",
        "id": 1567,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "pl-sql",
        "sub_category_id": 1173,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "PL/SQL",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "PL/SQL",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "ETL",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "etl",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "domain modeling",
          "alias_type": "CANONICAL",
          "id": 3675,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Domain Modeling",
          "alias_type": "CANONICAL",
          "id": 5644,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "domain modeling",
        "id": 2379,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "domain-modeling",
        "sub_category_id": 2831,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Application Architecture Patterns",
            "id": 293,
            "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
            "slug": "application-architecture-patterns",
            "source": "db"
          },
          "input_skill": "Data Modeling",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Service Architecture and Design Patterns",
            "id": 18,
            "rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
            "slug": "service-architecture-and-design-patterns",
            "source": "db"
          },
          "input_skill": "Data Modeling",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Data Modeling",
      "matched_via": "embedding_alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Dictionary",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-dictionary",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Quality",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-quality",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Governance",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Management",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "data-governance",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "ETL",
    "Data Dictionary",
    "Data Quality",
    "Data Governance"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Engineer",
    "id": 2,
    "rationale": "Exact alias hit on data-engineer (1.0) \u2014 no other alias at this confidence; skill_top data-engineer 0.25 does not contradict",
    "role_archetype": null,
    "slug": "data-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Snowflake",
      "tag": "in_db"
    },
    {
      "skill": "PL/SQL",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Data Dictionary",
      "tag": "new"
    },
    {
      "skill": "Data Quality",
      "tag": "new"
    },
    {
      "skill": "Data Governance",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Data Warehouses",
          "id": 22,
          "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
          "slug": "cloud-data-warehouses",
          "source": "db"
        },
        "dimension_id": 22,
        "input_skill": "Snowflake",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 105,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "PL/SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1567,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Application Architecture Patterns",
          "id": 293,
          "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
          "slug": "application-architecture-patterns",
          "source": "db"
        },
        "dimension_id": 293,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 2,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Service Architecture and Design Patterns",
          "id": 18,
          "rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
          "slug": "service-architecture-and-design-patterns",
          "source": "db"
        },
        "dimension_id": 18,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 2
  },
  "planner_output": null,
  "run_id": "1251e53c-b467-4dbc-b428-839243f1b2fc"
}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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