Pipeline run
12b9db3f-890a-4249-8373-50b069246f0d
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Underwater Robotics Ethnographer
CASE Aslug: — · id: — · source: llm
Resolution:
human_review_required
— role not in DB; role↔dimension links may be deferred.
Job description
Job Title: Underwater Robotics Ethnographer Company: PacificAI Research Institute Location: La Jolla, California, USA Experience: 5+ years field research About the Role: PacificAI Research Institute is hiring an Underwater Robotics Ethnographer to conduct qualitative field studies of autonomous underwater AI robotics deployments in Pacific coral reef ecosystems. You will join a multidisciplinary research team of marine biologists, AI ethicists, and oceanographers. Key Responsibilities: - Conduct ethnographic field studies aboard research vessels tracking AUV (autonomous underwater vehicle) deployments - Interview marine biologists, AI ethicists, and shipboard crew about deep-sea autonomous decision-making protocols - Observe and document human-AI collaboration patterns between AUV operators and onboard scientific staff - Author qualitative research papers on the social dynamics of remote-monitored marine AI systems - Contribute to interdisciplinary publications across anthropology, marine biology, and AI ethics journals - Present research at conferences on STS (Science and Technology Studies), oceanography, and AI policy Required Qualifications: - PhD or equivalent in cultural anthropology, STS, or qualitative sociology - 5+ years of ethnographic field experience preferably in scientific or industrial settings - Comfortable spending 4-6 weeks per year on research vessels in remote Pacific locations - Strong qualitative coding skills (NVivo, Atlas.ti) - Familiarity with AI ethics frameworks and STS methodology
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- AI (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Artificial Intelligence
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AI appears in a large and growing share of job descriptions across software, data, and product roles; major vendors like Microsoft, Google, and AWS have broad AI offerings and hiring demand reflects mainstream adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1020
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Social Sciences
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Biological Sciences
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Interdisciplinary Studies
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Earth Sciences
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Ethics in Technology
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Ethics in Technology
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| AI | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | AUV | type=Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Anthropology | type=Social Sciences subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Marine Biology | type=Biological Sciences subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | Science and Technology Studies | type=Interdisciplinary Studies subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Oceanography | type=Earth Sciences subtype=general nature=CONCEPT lifespan=EVERGREEN | |
| canonical_skill_proposed | AI Ethics | type=Ethics in Technology subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | AI Policy | type=Ethics in Technology subtype=general nature=CONCEPT lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "PacificAI Research Institute",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Field Research",
"Ethnographic Research"
],
"domain": "Research"
},
"secondary": null
},
"education": [
{
"level": "Doctorate",
"qualification": "PhD - Cultural Anthropology",
"raw": "PhD or equivalent in cultural anthropology, STS, or qualitative sociology",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 5,
"raw": "5+ years field research"
},
"job_locations": [
{
"aliases": [
"San Diego",
"La Jolla, CA"
],
"city": "La Jolla",
"country": "USA",
"state": "California",
"work_mode": null
}
],
"role": "Underwater Robotics Ethnographer",
"role_aliases": [
"Ethnographer",
"Field Researcher",
"Cultural Anthropologist"
],
"role_archetype": "Research",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Key Responsibilities: - Conduct ethnographic",
"last_5_words": "oceanography, and AI policy"
},
"text": "- Conduct ethnographic field studies aboard research vessels tracking AUV (autonomous underwater vehicle) deployments\n- Interview marine biologists, AI ethicists, and shipboard crew about deep-sea autonomous decision-making protocols\n- Observe and document human-AI collaboration patterns between AUV operators and onboard scientific staff\n- Author qualitative research papers on the social dynamics of remote-monitored marine AI systems\n- Contribute to interdisciplinary publications across anthropology, marine biology, and AI ethics journals\n- Present research at conferences on STS (Science and Technology Studies), oceanography, and AI policy",
"word_count": 90
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "AUV"
},
{
"is_primary": true,
"skill_name": "AI"
},
{
"is_primary": true,
"skill_name": "Anthropology"
},
{
"is_primary": true,
"skill_name": "Marine Biology"
},
{
"is_primary": true,
"skill_name": "Science and Technology Studies"
},
{
"is_primary": true,
"skill_name": "Oceanography"
},
{
"is_primary": true,
"skill_name": "AI Ethics"
},
{
"is_primary": true,
"skill_name": "AI Policy"
}
],
"jd_role": {
"display_name": "Underwater Robotics Ethnographer",
"rationale": null,
"role_aliases": [
"Ethnographer",
"Field Researcher",
"Cultural Anthropologist"
],
"role_archetype": "Research",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "PacificAI Research Institute",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Field Research",
"Ethnographic Research"
],
"domain": "Research"
},
"secondary": null
},
"education": [
{
"level": "Doctorate",
"qualification": "PhD - Cultural Anthropology",
"raw": "PhD or equivalent in cultural anthropology, STS, or qualitative sociology",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 5,
"raw": "5+ years field research"
},
"job_locations": [
{
"aliases": [
"San Diego",
"La Jolla, CA"
],
"city": "La Jolla",
"country": "USA",
"state": "California",
"work_mode": null
}
],
"role": "Underwater Robotics Ethnographer",
"role_aliases": [
"Ethnographer",
"Field Researcher",
"Cultural Anthropologist"
],
"role_archetype": "Research",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Key Responsibilities: - Conduct ethnographic",
"last_5_words": "oceanography, and AI policy"
},
"text": "- Conduct ethnographic field studies aboard research vessels tracking AUV (autonomous underwater vehicle) deployments\n- Interview marine biologists, AI ethicists, and shipboard crew about deep-sea autonomous decision-making protocols\n- Observe and document human-AI collaboration patterns between AUV operators and onboard scientific staff\n- Author qualitative research papers on the social dynamics of remote-monitored marine AI systems\n- Contribute to interdisciplinary publications across anthropology, marine biology, and AI ethics journals\n- Present research at conferences on STS (Science and Technology Studies), oceanography, and AI policy",
"word_count": 90
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "12b9db3f-890a-4249-8373-50b069246f0d",
"stage3_signals": {
"alias_found": false,
"alias_match_roles": [],
"kra_match_roles": [
{
"display_name": "AI Compliance Officer",
"kra_matches": [
{
"kra_text": "Reviews AI use cases and model deployments against applicable regulations, internal ethics policies, and governance guidelines prior to production approval.",
"sentence": "Interview marine biologists, AI ethicists, and shipboard crew about deep-sea autonomous decision-making protocols",
"similarity": 0.4649
},
{
"kra_text": "Monitors deployed AI systems for compliance policy drift, regulatory changes, and emerging requirements affecting existing AI deployments.",
"sentence": "Observe and document human-AI collaboration patterns between AUV operators and onboard scientific staff",
"similarity": 0.4591
},
{
"kra_text": "Monitors deployed AI systems for compliance policy drift, regulatory changes, and emerging requirements affecting existing AI deployments.",
"sentence": "Author qualitative research papers on the social dynamics of remote-monitored marine AI systems",
"similarity": 0.4348
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 12,
"score": 0.4529,
"slug": "ai-compliance-officer",
"total_count": null
},
{
"display_name": "AI Engineer",
"kra_matches": [
{
"kra_text": "Documents AI feature capabilities, known limitations, failure modes, prompt versioning, and operational runbooks for engineering and product teams.",
"sentence": "Observe and document human-AI collaboration patterns between AUV operators and onboard scientific staff",
"similarity": 0.4314
},
{
"kra_text": "Integrates AI model API responses with application business logic, database writes, event publishing, and downstream service orchestration.",
"sentence": "Contribute to interdisciplinary publications across anthropology, marine biology, and AI ethics journals",
"similarity": 0.409
},
{
"kra_text": "Defines evaluation frameworks, automated test suites, and human feedback loops to measure AI feature quality, accuracy, and consistency.",
"sentence": "Author qualitative research papers on the social dynamics of remote-monitored marine AI systems",
"similarity": 0.3656
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 0.402,
"slug": "ai-engineer",
"total_count": null
},
{
"display_name": "AR/VR Engineer",
"kra_matches": [
{
"kra_text": "Debugs visual artifacts, tracking jitter, controller input latency, and interaction edge cases in immersive XR application experiences.",
"sentence": "Observe and document human-AI collaboration patterns between AUV operators and onboard scientific staff",
"similarity": 0.4049
},
{
"kra_text": "Debugs visual artifacts, tracking jitter, controller input latency, and interaction edge cases in immersive XR application experiences.",
"sentence": "Conduct ethnographic field studies aboard research vessels tracking AUV (autonomous underwater vehicle) deployments",
"similarity": 0.338
},
{
"kra_text": "Develops room-scale locomotion systems, 6DoF interaction mechanics, and comfort-aware movement patterns for immersive VR experiences.",
"sentence": "Interview marine biologists, AI ethicists, and shipboard crew about deep-sea autonomous decision-making protocols",
"similarity": 0.3268
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 8,
"score": 0.3566,
"slug": "ar-vr-engineer",
"total_count": null
},
{
"display_name": "ML Ops Engineer",
"kra_matches": [
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Observe and document human-AI collaboration patterns between AUV operators and onboard scientific staff",
"similarity": 0.3251
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Interview marine biologists, AI ethicists, and shipboard crew about deep-sea autonomous decision-making protocols",
"similarity": 0.3169
},
{
"kra_text": "Maintains model versioning, experiment lineage, and artifact tracking using MLflow, DVC, or Weights \u0026 Biases for reproducibility and auditability.",
"sentence": "Contribute to interdisciplinary publications across anthropology, marine biology, and AI ethics journals",
"similarity": 0.3029
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.315,
"slug": "ml-ops-engineer",
"total_count": null
},
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Monitors production model behavior for data drift, concept drift, and prediction performance degradation using monitoring dashboards and alerting.",
"sentence": "Observe and document human-AI collaboration patterns between AUV operators and onboard scientific staff",
"similarity": 0.3424
},
{
"kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
"sentence": "Contribute to interdisciplinary publications across anthropology, marine biology, and AI ethics journals",
"similarity": 0.286
},
{
"kra_text": "Builds model serving infrastructure to deploy trained models as real-time prediction APIs or batch inference jobs using TorchServe, TensorFlow Serving, or SageMaker.",
"sentence": "Present research at conferences on STS (Science and Technology Studies), oceanography, and AI policy",
"similarity": 0.2799
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.3028,
"slug": "ml-engineer",
"total_count": null
}
],
"skill_match_roles": []
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
"chosen_role": {
"display_name": "AI Compliance Officer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 12,
"score": 0.4529,
"slug": "ai-compliance-officer",
"total_count": null
},
"confidence": 0.384965,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Top KRA ai-compliance-officer stands; no contradicting signal"
},
"stage5_updates": {
"centroid_n_after": 9,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 2195,
"role_display_name": "AI Compliance Officer",
"role_slug": "ai-compliance-officer",
"skill_name": "AUV",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 2196,
"role_display_name": "AI Compliance Officer",
"role_slug": "ai-compliance-officer",
"skill_name": "Anthropology",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 2197,
"role_display_name": "AI Compliance Officer",
"role_slug": "ai-compliance-officer",
"skill_name": "Marine Biology",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 2198,
"role_display_name": "AI Compliance Officer",
"role_slug": "ai-compliance-officer",
"skill_name": "Science and Technology Studies",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 2199,
"role_display_name": "AI Compliance Officer",
"role_slug": "ai-compliance-officer",
"skill_name": "Oceanography",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 2200,
"role_display_name": "AI Compliance Officer",
"role_slug": "ai-compliance-officer",
"skill_name": "AI Ethics",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 2201,
"role_display_name": "AI Compliance Officer",
"role_slug": "ai-compliance-officer",
"skill_name": "AI Policy",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1990,
"existing_alias_text": "AI",
"input_term": "AI",
"matched_canonical": {
"category_id": 2,
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"id": 1347,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "ai",
"sub_category_id": 1020,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [],
"chosen_role": {
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"id": null,
"rationale": null,
"role_archetype": "Research",
"slug": "",
"source": "llm"
},
"dimensions": [
{
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"display_name": "React Frontend Development",
"id": 96,
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"slug": "d_init_01",
"source": "db"
},
"input_skill": "AI",
"llm_role": null,
"roles_from_db": []
}
],
"input_final_skills": [
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"Science and Technology Studies",
"Oceanography",
"AI Ethics",
"AI Policy"
],
"input_llm_skills": [
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"new_aliases_persisted": 0,
"run_id": "12b9db3f-890a-4249-8373-50b069246f0d",
"skills_detail": [
{
"aliases_in_db": [],
"canonical": null,
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"input_skill": "AUV",
"matched_via": null,
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"new_skill_meta": {
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{
"aliases_in_db": [
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],
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{
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"new_skill_meta": {
"derived": {
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"merge_log": [],
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},
"source_tag": "llm",
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},
{
"aliases_in_db": [],
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"input_skill": "Marine Biology",
"matched_via": null,
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"new_skill_meta": {
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"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "science-and-technology-studies",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Oceanography",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Earth Sciences",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "oceanography",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "AI Ethics",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Ethics in Technology",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "ai-ethics",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "AI Policy",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Ethics in Technology",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "ai-policy",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"AUV",
"Anthropology",
"Marine Biology",
"Science and Technology Studies",
"Oceanography",
"AI Ethics",
"AI Policy"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "Underwater Robotics Ethnographer",
"id": null,
"rationale": null,
"role_archetype": "Research",
"slug": "",
"source": "llm"
},
"chosen_role_resolution": "human_review_required",
"final_input_skills": [
{
"skill": "AUV",
"tag": "new"
},
{
"skill": "AI",
"tag": "in_db"
},
{
"skill": "Anthropology",
"tag": "new"
},
{
"skill": "Marine Biology",
"tag": "new"
},
{
"skill": "Science and Technology Studies",
"tag": "new"
},
{
"skill": "Oceanography",
"tag": "new"
},
{
"skill": "AI Ethics",
"tag": "new"
},
{
"skill": "AI Policy",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": null,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "AI",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1347,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 0
},
"planner_output": null,
"run_id": "12b9db3f-890a-4249-8373-50b069246f0d"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.