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Pipeline run

af970cda-d385-45c5-80ce-21456517392d

Pipeline LLM cost (USD)

API 1: $0.0017 API 2: $0.0000 API 3: $0.0000 Total: $0.0017

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: extract_from_jd_done Created: 2026-05-27T17:15:51.739892Z Updated: 2026-05-27T17:15:51.739892Z

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

No chosen role stored for this run.

Job description

Job Description:

• Establishes database management systems, standards, guidelines, and quality assurance for database deliverables, such as conceptual design, logical database, capacity planning, external data interface specification, data loading plan, data maintenance plan and security policy. Documents and communicates database design. 
• Evaluates and installs database management systems. 
• Codes complex programs and derives logical processes on technical platforms. Builds windows, screens, and reports. Assists in the design of user interface and business application prototypes.
• Participates in quality assurance and develops test application code in client server environment. 
• Provides expertise in devising, negotiating, and defending the tables and fields provided in the database. Adapts business requirements, developed by modeling/development staff and systems engineers, and develops the data, database specifications, and table and element attributes for an application.
• At more experienced levels, helps to develop an understanding of client's original data and storage mechanisms. 
• Determines appropriateness of data for storage and optimum storage organization. 
• Determines how tables relate to each other and how fields interact within the tables for a relational model.

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); body has only 0 canonical-skill mentions and 0 tech-marker hits \u2014 insufficient evidence",
    "role_aliases": [],
    "role_archetype": "\u2014",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "fail"
  },
  "rejected": true,
  "rejection_reason": "Stage 1 marked JD_type=fail (unparseable); body has only 0 canonical-skill mentions and 0 tech-marker hits \u2014 insufficient evidence",
  "run_id": null,
  "stage3_signals": null,
  "stage4_decision": null,
  "stage5_updates": null
}

API 2 — extract-details

{}

API 3 — final-role-output

{}