Individual skill
investigation-review-gap-dashboard
Create structured JSON legal investigation gap, remediation, retention, privilege, QC, and production-readiness dashboards from an Investigation Review Hub style source of record and a task-local answer template. Use when a prompt asks for matter-specific production gap analysis, subpoena category coverage, retention or preservation gap review, privilege-log corrections, QC remediation, available archive/source analysis, or prioritized action plans in a prescribed JSON schema.
experiments/codex_gpt5_5_xhigh/reports/skills/task_group_017/reflect-3/reflect-3_attempt_02/SKILL.md
Source commit 5082a777379156a048881e94940d640702e5e09a
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 6, 2026
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Repository context
- Repository stars (not skill ratings)
- 121
- Repository last updated
- Oct 29, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent Repository topic
- ai Repository topic
- benchmark Repository topic
- llm Repository topic
- self-evolving Repository topic
- self-learning Repository topic
- skills Repository topic
- legal Derived category
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Read excerpt
# Investigation Review Gap Dashboard
## Core Workflow
1. Read the prompt, task-local answer template, and any task-local matter or request context. Extract the matter ID, deliverable type, required top-level keys, enum choices, required item fields, metric definitions, and ordering rules.
2. Use the task-designated Investigation Review Hub as the source of record. Do not inspect local environment source files, database files, generated manifests, hidden notes, answer files, or evaluation artifacts.
3. Filter every evidence pull by the requested matter ID. Treat similarly named records from other matters as contamination for the answer.
4. Build the answer from stable hub IDs. Prefer records targeted by non-noise remediation actions, explicit issue tags, exception notes, QC findings, privilege entries, retention events, and source records over routine production noise.
5. Populate only the schema requested by the local answer template. Use enum values exactly as written in the template, use integers for counts, use `null` for unknown scalar values, and use empty arrays where a list has no values.
6. Sort every list exactly as the template says before final output. Return one JSON object and no prose.
## Evidence Selection
Start with remediation/action targets when present. Exclude rows marked as routine, noise, ordinary variance, resolved, no unresolved production impact, or not immediate remediation unless the prompt or metric definition explicitly asks for broad rollups.