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Pipeline run

fcc67b2e-3c10-4b60-966a-48aabe71710f

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: 2026-05-27T14:24:47.674256Z Updated: 2026-05-27T14:24:47.674256Z

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

We have great opportunity for you if interested then send me your updated CV on soham.hr.hi@gmail.com


Technology/AreaMandatory SkillsValue adding skillsJava Core Java/AOOPsTCP/IP Socket APIsSpring HibernateDatabasePersistent DB• Develop stored procedures based on PL/SQL1. MySQL2. Oracle In Memory1. HazelcastWeb TechnologiesWeb ServicesHTML5, CSS1. RestAngular JS2. SoapJQueryJavaScriptELK StackJSONScriptingN/APythonShellAWKGroovyUnit Test ToolsN/AMockitoJMockitJunitTools API Manager FrameworkN/A1. WSO2DocumentationExcellent EnglishN/AExcellent Documentation skillsOperations ManualsOS and NetworkingLinux (RedHat / CentOS)Basic understanding of networking concepts Agile MethodologiesScrum FrameworkN/A

Library artifacts (this run)

No artifact rows for this run.

API 1 — extract-from-jd click to toggle

{
  "final_skills": [],
  "jd_role": {
    "display_name": "We have great opportunity for you if interested then send me your updated CV on soham.hr.hi@gmail.com",
    "rationale": "JD body too sparse: 50 words, 1 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: 50 words, 1 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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