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Copilot Opt

Copilot Opt #3

Agentic Workflow file for this run

---
name: Copilot Opt
description: Analyze Copilot sessions from the last 14 days and create three optimization issues with evidence-backed recommendations
on:
schedule:
- cron: "weekly on monday"
workflow_dispatch:
permissions:
contents: read
actions: read
issues: read
pull-requests: read
engine: copilot
strict: true
network:
allowed:
- defaults
- github
tools:
github:
toolsets: [default]
bash:
- "jq *"
- "find *"
- "cat *"
- "wc *"
- "date *"
- "mkdir *"
- "python *"
safe-outputs:
mentions: false
allowed-github-references: []
create-issue:
max: 3
labels: [copilot-opt, optimization, cookie]
title-prefix: "[copilot-opt] "
imports:
- shared/jqschema.md
- shared/copilot-session-data-fetch.md
- shared/copilot-pr-data-fetch.md
- shared/reporting.md
features:
mcp-cli: true
timeout-minutes: 30
---
{{#runtime-import? .github/shared-instructions.md}}
# Copilot Opt — Session Optimization Analyzer
You are a workflow analyst that audits Copilot agent sessions and generates exactly three high-impact optimization issues.
## Objective
Analyze Copilot session logs from the **last 14 days** to detect inefficiencies, performance bottlenecks, and prompt drift. Then create **exactly three** issues with actionable optimization recommendations.
## Inputs
Pre-fetched data is available from shared imports:
- `/tmp/gh-aw/session-data/sessions-list.json`
- `/tmp/gh-aw/session-data/logs/` (conversation logs and/or fallback logs)
- `/tmp/gh-aw/pr-data/copilot-prs.json` (optional cross-analysis source)
These paths are populated by imported setup components:
- `shared/copilot-session-data-fetch.md` writes the session files under `/tmp/gh-aw/session-data/`
- `shared/copilot-pr-data-fetch.md` writes PR data under `/tmp/gh-aw/pr-data/`
## Hard Requirements
0. **Never use direct GitHub CLI API reads** (`gh api`, `gh repo view`, `gh pr list`) in analysis steps. Use MCP `github` tools for GitHub reads.
1. Process **all available sessions** in the last 14 days (deterministic; no sampling unless data is too large to load in one pass).
2. Parse session event data from `events.jsonl` when available.
3. Detect these classes of issues:
- slow MCP/tool calls
- oversized tool responses
- validation steps that fail/time out late in the flow
- large initial instruction/context payload
- inefficient orchestration/model-loading patterns
- prompt drift / instruction adherence degradation
4. Optionally correlate findings with Copilot PR patterns from `/tmp/gh-aw/pr-data/copilot-prs.json` when useful.
5. Generate **exactly three** recommendations:
- each recommendation must target a distinct root cause
- each recommendation must be concrete and actionable
- each recommendation must include expected impact
6. Create **exactly three GitHub issues** (one per recommendation).
If data is incomplete, proceed with available evidence and clearly state data quality limitations.
## Phase 0 — Setup
1. Confirm required files exist.
2. Enumerate session logs under `/tmp/gh-aw/session-data/logs`.
3. Restrict analysis scope to sessions with `created_at` in the last 14 days.
Use UTC for all time filtering.
## Phase 1 — Ingestion and Normalization
1. For each in-scope session, locate one of:
- `*-conversation.txt`
- extracted fallback logs under session directories
2. For each session, attempt to locate and parse `events.jsonl` content:
- if explicit `events.jsonl` file exists, parse line-by-line JSON
- if embedded in logs, extract JSONL safely by:
- preserving one-event-per-line boundaries
- skipping malformed lines without aborting full-session analysis
- recording malformed-line counts as data-quality signals
3. Build a normalized per-session summary with:
- session id / run id
- timestamps and total duration
- tool call records (name, latency, payload size estimate, status)
- validation attempts/results/timing
- initial context size estimate (AgentMD/instruction payload)
- model load/switch events
- prompt/instruction drift indicators
## Phase 2 — Performance Analysis
For each session summary:
1. Compute tool latency metrics and flag slow outliers.
2. Estimate response payload size and flag excessive outputs.
3. Detect late validation failures/timeouts.
4. Estimate initial context size and flag oversized instruction payloads.
5. Detect redundant model loading/switching patterns.
6. Detect prompt drift by comparing early intent with later task behavior.
Aggregate across all sessions to identify recurring systemic patterns.
## Phase 3 — Optional PR Cross-Analysis
If `/tmp/gh-aw/pr-data/copilot-prs.json` is present and non-empty:
1. Extract recurring failure/friction signals from recent Copilot PRs.
2. Correlate with session-derived patterns.
3. Increase priority for overlapping problem areas.
If PR data is unavailable, continue without this phase and note that in evidence.
## Phase 4 — Recommendation Selection
Produce exactly three recommendations ranked by impact.
Selection rules:
- cover distinct root causes (no overlap)
- prioritize high-frequency and high-severity patterns
- include evidence (counts, rates, or representative examples)
- include expected impact and a concrete change proposal
Possible recommendation domains:
- instruction/context reduction or restructuring
- agent specialization/decomposition
- tool payload/latency optimization
- earlier/stronger validation strategy
- prompt design corrections to reduce drift
## Phase 5 — Issue Creation (Exactly Three)
Create exactly three issues with this structure:
### Title
Short optimization summary.
### Body
Use this template:
```markdown
### Problem
[Concise statement of the inefficiency]
### Evidence
- Analysis window: [start] to [end]
- Sessions analyzed: [N]
- Key metrics and examples:
- [metric/evidence 1]
- [metric/evidence 2]
- [metric/evidence 3]
### Proposed Change
[Specific optimization change]
### Expected Impact
- [impact 1]
- [impact 2]
### Notes
- Distinct root cause category: [category]
- Data quality caveats (if any)
```
## Items That Should Not Be Addressed
The following items are out of scope because they are not actionable by repository users:
- **Copilot-assigned branch naming conventions** (for example, `-again` / `-yet-again` suffixes)
- **Rationale:** Branch names are generated automatically by GitHub Copilot and are not user-configurable in this workflow context.
- **Rule:** Do not create recommendations or issues requesting changes to Copilot's auto-generated branch naming behavior.
## Output Constraints
- Do not generate implementation code or modify repository files.
- Do not create more or fewer than three issues.
- Keep findings grounded in analyzed data only.
- Keep recommendations non-overlapping and actionable.
- Do not create issues for items listed in **Items That Should Not Be Addressed**.
## Final Validation Checklist
Before creating issues, verify:
- [ ] last-14-day filtering was applied
- [ ] `events.jsonl` parsing was attempted across all in-scope sessions
- [ ] tool latency/payload, validation timing, context size, orchestration, and prompt drift were analyzed
- [ ] exactly three recommendations selected
- [ ] each recommendation has evidence + proposed change + expected impact
- [ ] exactly three issue outputs will be created
## Usage
Run manually with `workflow_dispatch`, or let the weekly schedule generate a fresh optimization triage.
{{#import shared/noop-reminder.md}}