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/analyze-copilot-sessions

@ecefb68
by yamapanaktsmm/agent-skills26 stars
4

Analyze historical VS Code GitHub Copilot Chat sessions by model, reasoning effort, AIU, time, reliability, workflow behavior, and external quality evidence, or safely prune workspace-scoped local chat history by age. Use for session analysis, repeated-task retrospectives, model evaluation, cost/performance analysis, and old session cleanup.

Use this Skill: https://skilld.dev/gh/aktsmm/agent-skills/analyze-copilot-sessions

This session only. Nothing lands on disk.

referencesquality-evidence.md

≈716 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Quality Evidence

Logs measure activity, not correctness. Bind quality through an analysis manifest and external persisted artifacts.

Report observed execution state separately from durable outcome state. A canceled or stalled UI/parent request does not make persisted artifacts fail, and successful child activity does not make the workflow pass without declared artifact identity and gate evidence. Leave retry and partition reconciliation to the owning workflow rather than inferring it from session lifecycle.

Levels

Level Requirement Interpretation
VERIFIED Independent evaluation artifact plus declared gate paths Strongest available evidence
GATE_SUPPORTED Persisted test, validator, completion, or final-gate artifacts Workflow quality passed
PROXY_ONLY Explicit proxy such as manual acceptance, without persisted gate Not an accuracy claim
UNMEASURED No external evidence Excluded from quality ranking

VERIFIED requires at least one gate path. A missing, unknown, or failed gate does not pass. Fix counts and low error rates are context only.

Analysis Manifest

Paths are relative to the manifest location unless absolute paths are supplied. Prefer relative paths for portability.

{
  "schema_version": 1,
  "runs": [
    {
      "label": "model-a-high",
      "metrics_path": "metrics/model-a.json",
      "workload": {
        "task_kind": "detailed-review",
        "unit_name": "question",
        "unit_count": 30,
        "revision": "content-sha256",
        "workflow_version": "v2",
        "rubric_version": "2026-07"
      },
      "quality_evidence": {
        "level": "GATE_SUPPORTED",
        "gate_paths": ["evidence/completion.json", "evidence/validator.json"],
        "adjudicated_residual_defects": 0,
        "notes": ["All declared final gates passed."]
      }
    }
  ]
}

Gate Status

The adapter recognizes top-level final_verdict, verdict, status, or overall_status, and nested result. Only explicit PASS or FAIL is decisive. Unknown status is counted as a failed/unknown gate.

Detailed Review Adapter

For a formal review, bind the run metrics to current completion and validator artifacts. Evidence validation can be an additional gate. Use the reviewed question count as unit_count; never infer correctness from the number of fixes.

When comparing a rerun to an older run, use the same content revision if the goal is a model A/B. If the CSV or rubric changed, preserve the distinct revision and allow comparability to fall to MEDIUM or LOW.

Source: SKILL.md on GitHub

1 warning1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    This skill analyzes and manages local VS Code Copilot Chat session history. It extracts performance and cost metrics from debug logs and provides tools to prune old history from local storage and internal databases. It includes strong privacy protections, such as filtering out conversation content from metrics analysis, and performs all operations locally without external network transmission.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: MEDIUM · 1 issue

Signed by skilld at ecefb68. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 18 hours ago.

Activeupdated 2 months ago
user-invocable
true
metadata
{
  "author": "yamapan (https://github.com/aktsmm)"
}
Other metadata
argument-hint
session IDs, log paths, metrics JSON, task/workload unit, quality evidence, and analysis focus

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