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
—
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)
Skill cluster (0 dimension groups, role-scoped)
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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