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Pipeline run

2736bc6c-9d23-4915-b0bd-50d9f9f6c3b5

Pipeline LLM cost (USD)

API 1: $0.0016 API 2: $0.0000 API 3: $0.0000 Total: $0.0016

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description

SPARSE JD

Nature of work

—

no_db_connection

Tech stack maturity

Mainstream Modern

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 (0)

No skills extracted

Skill cluster (0 dimension groups, role-scoped)

No dimension groups computed for this JD.

Status: completed Created: 2026-05-20T19:41:36.873039Z Updated: 2026-05-20T19:41:36.994220Z API 3 duration: 29 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

—

slug: — · id: — · source: llm

Stage 1 marked JD_type=fail (unparseable)

Resolution: human_review_required — role not in DB; role↔dimension links may be deferred.

New skills

Skill↔dim saved

Role↔dim saved

Skipped

Job description

Proficiency in MuleSoft Anypoint Studio, including its design, development, and deployment capabilities.
APIs: Strong understanding of API design principles, RESTful APIs, SOAP APIs, and API management practices.
Data Transformation: Experience with DataWeave for transforming and mapping data between different formats.
Java and Related Technologies: A foundational understanding of Java and Java-related technologies as Mulesoft is built on Java.
Integration Patterns: Knowledge of various integration patterns (e.g., message-oriented middleware, event-driven architecture).
Databases: Familiarity with database concepts and technologies.
JSON and XML: Good understanding of JSON and XML formats.

Library artifacts (this run)

No artifact rows for this run.

nano JD Parser — gpt-4.1-nano click to toggle

JD type fail

Show raw JSON

{
  "JD_type": "fail"
}

API 1 — extract-from-jd click to toggle

{
  "final_skills": [],
  "jd_role": {
    "display_name": "\u2014",
    "rationale": "Stage 1 marked JD_type=fail (unparseable)",
    "role_aliases": [],
    "role_archetype": "\u2014",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "fail"
  },
  "rejected": true,
  "rejection_reason": "Stage 1 marked JD_type=fail (unparseable)",
  "run_id": null,
  "stage3_signals": null,
  "stage4_decision": null,
  "stage5_updates": null
}

API 2 — extract-details

{
  "alias_matches": [],
  "candidate_roles": [],
  "chosen_role": {
    "display_name": "\u2014",
    "id": null,
    "rationale": "Stage 1 marked JD_type=fail (unparseable)",
    "role_archetype": "\u2014",
    "slug": "",
    "source": "llm"
  },
  "dimensions": [],
  "input_final_skills": [],
  "input_llm_skills": [],
  "new_aliases_persisted": 0,
  "run_id": "2736bc6c-9d23-4915-b0bd-50d9f9f6c3b5",
  "skills_detail": [],
  "unmatched_skills": []
}

API 3 — final-role-output

{
  "chosen_role": {
    "display_name": "\u2014",
    "id": null,
    "rationale": "Stage 1 marked JD_type=fail (unparseable)",
    "role_archetype": "\u2014",
    "slug": "",
    "source": "llm"
  },
  "chosen_role_resolution": "human_review_required",
  "final_input_skills": [],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "2736bc6c-9d23-4915-b0bd-50d9f9f6c3b5"
}

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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