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Pipeline run

12152b88-3db2-438c-84a2-87a31e6758f7

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: 2026-05-27T15:56:43.921252Z Updated: 2026-06-12T15:43:12.281796Z

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

Effective requirement analysis skills, should be able to communicate with the business stakeholders for requirement clarifications, analysis

Should be able to prepare low level designs for the modules/application

Object oriented analysis and design skills are absolutely needed

The resource should be able to write effective code, do peer reviews and document the unit test cases

Writing automation test cases using JUNIT

Nice to have AWS knowledge in terms of deployment, configurations

Should take proactive participation is deployment to test, pre- prod and production environments for the product

Should be able to present the application to the business stakeholders

This job is provided by Shine.com

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

{}

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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