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Pipeline run

7163fb89-ef17-4061-a591-3057e0b37474

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

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: 2026-05-27T15:15:06.964210Z Updated: 2026-06-12T16:40:11.743695Z
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

The ideal candidate will be responsible for conceptualizing and executing clear, quality code to develop the best software. You will test your code, identify errors, and iterate to ensure quality code. You will also support our customers and partners by troubleshooting any of their software issues.

Responsibilities
Detect and troubleshoot software issues
Write clear quality code for software and applications and perform test reviews
Develop, implement, and test APIs
Provide input on software development projects


Qualifications
Comfort using programming languages and relational databases
Strong debugging and troubleshooting skills
1 Year Experience
Fresher can also apply

Library artifacts (this run)

No artifact rows for this run.
API 1 — extract-from-jd click to toggle
{
  "final_skills": [],
  "jd_role": {
    "display_name": "The ideal candidate will be responsible for conceptualizing and executing clear, quality code to develop the best software. You will test your code, identify errors, and iterate to ensure quality code. You will also support our customers and partners by troubleshooting any of their software issues.",
    "rationale": "JD body too sparse: 49 words, 0 tech-marker hits \u2014 needs more detail (\u003e=80 words or \u003e=2 tech markers) for confident classification",
    "role_aliases": [],
    "role_archetype": "Other",
    "slug": ""
  },
  "nano_parsed": null,
  "rejected": true,
  "rejection_reason": "Sparse JD: JD body too sparse: 49 words, 0 tech-marker hits \u2014 needs more detail (\u003e=80 words or \u003e=2 tech markers) for confident classification",
  "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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