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chore: clean up stale ralph dirs, orphaned docs, and add Cursor rules
- Remove all 20 completed ralph directories (all stories passed) - Remove root-level ralph state (prd.json, progress.txt) - Remove stale leaderboard snapshots (LEADERBOARD_RESULTS.md, leaderboard.json) - Archive 10 orphaned docs (zero references) to docs/archive/ - Update .gitignore: drop ralph-ent-governance exception, add tmp/cache/eval_reports - Add .cursor/rules/ with 10 skill files from ~/.claude/skills for Cursor IDE - Fix false-positive docs consistency check in EXTENSIBILITY.md Co-authored-by: Cursor <cursoragent@cursor.com>
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‎.cursor/rules/agent-delegation.mdc‎

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---
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description: Agent delegation skills — route and delegate tasks to the best AI agent (Codex, Cursor, Gemini, Copilot) based on task type and complexity. Use when asked to delegate work to other AI agents.
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---
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# Delegate Skill
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Routes coding tasks to the optimal AI CLI agent and delegates execution.
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## Usage
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```
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/delegate <task description>
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```
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## Routing
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Run the CLI router:
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```bash
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python3 ~/agent-skills/router-service/route_cli.py "<task>"
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```
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Options: `--prefer-speed`, `--prefer-cost`, `--compact`, `--exclude codex cursor`
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## Agent Capabilities
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| Agent | Role | Best For | Model |
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|-------|------|----------|-------|
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| **copilot** | Opus Specialist | Complex reasoning, security analysis, deep reviews | claude-opus-4.5 |
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| **cursor** | Structural Work | Planning, architecture, refactoring, debugging, code review | --model auto |
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| **gemini** | Code Gen Engine | Code generation, research, documentation | gemini-3-pro |
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| **codex** | Misc Coding | Algorithms, utility scripts | gpt-5.2-codex |
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## Task Type Routing
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| Task Type | Agent | Why |
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|-----------|-------|-----|
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| planning/architecture | Cursor | Plan mode for structured planning |
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| code_review | Cursor | Ask mode for thorough review |
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| security_review | Copilot | Opus for deep security analysis |
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| refactoring | Cursor | Agent mode, repo-aware |
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| debugging | Cursor | Systematic debugging |
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| debugging_complex | Copilot | Race conditions, concurrency |
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| code_generation | Gemini | Primary code gen |
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| research/exploration | Gemini | Codebase analysis |
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| documentation | Gemini | Clear, structured docs |
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| algorithms | Codex | Algorithmic optimization |
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## Execution Steps
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1. Route task via CLI
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2. Parse JSON response (selected_agent, confidence, reasoning)
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3. Inform user and IMMEDIATELY delegate via Task tool
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4. Wait for driver agent to complete (handles orchestration)
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5. Return compressed results
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## Fallback
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- Default to gemini for research/analysis
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- Default to cursor for code modification
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---
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# Codex CLI Guide
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## Command Templates
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```bash
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export PATH="/opt/homebrew/bin:$HOME/.local/bin:$PATH" && codex exec --skip-git-repo-check --sandbox <MODE> "<prompt>" 2>/dev/null
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```
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## Sandbox Modes
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| Task Type | Mode |
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|-----------|------|
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| Analysis, Q&A | `--sandbox read-only` |
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| Create/edit files | `--sandbox workspace-write --full-auto` |
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| Network access | `--sandbox danger-full-access --full-auto` |
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## Models
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- Default: `-m gpt-5.2-codex`
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- General: `-m gpt-5.2`
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- Reasoning: `--config model_reasoning_effort="xhigh|high|medium|low"`
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---
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# Cursor CLI Guide
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**The CLI command is `agent`, NOT `cursor`.**
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## Command Templates
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```bash
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export PATH="$HOME/.local/bin:$PATH" && agent -p "<prompt>" --model auto --output-format text
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```
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## Modes
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- `ask` — read-only analysis
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- `agent` — full file editing
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- `plan` — planning without execution
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- Default: auto-selected by task type
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## Models
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- `--model auto` (recommended), `--model gpt-5.2`, `--model gemini-3-flash`, `--model opus-4.5-thinking`
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---
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# Copilot CLI Guide
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## Command Templates
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```bash
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export PATH="$HOME/.local/share/mise/installs/node/24.13.0/bin:$HOME/.local/bin:$PATH" && copilot -p "<prompt>" --allow-all-paths
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```
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## Permission Modes
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| Task Type | Flags |
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|-----------|-------|
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| Analysis, Q&A | (none) |
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| File edits | `--allow-all-paths` |
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| URL access | `--allow-all-urls` |
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## Models
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- Default: `--model claude-sonnet-4.5`
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- Fast: `--model claude-haiku-4.5`
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- Complex: `--model claude-opus-4.5`
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---
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# Gemini CLI Guide
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## Command Templates
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```bash
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export PATH="$HOME/.local/share/mise/installs/node/24.13.0/bin:$HOME/.local/bin:$PATH" && NODE_OPTIONS="--no-warnings" gemini -p "<prompt>" --yolo
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```
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## Modes
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- Read-only: no flags
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- File edits: `--yolo`
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- Controlled: `--approval-mode auto_edit`
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## Models
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- Default: `gemini-2.5-pro`
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- Fast: `-m gemini-2.5-flash`
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## Session Management
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- `--list-sessions`, `--resume latest`, `--resume <id>`

‎.cursor/rules/ccb-analysis.mdc‎

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---
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description: CCB analysis skills — compare configs, audit MCP usage, IR quality metrics, cost analysis, and trace evaluation. Use when analyzing benchmark results, comparing configurations, or investigating MCP impact.
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globs:
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- scripts/compare_configs.py
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- scripts/mcp_audit.py
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- scripts/ir_analysis.py
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- scripts/cost_report.py
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- scripts/audit_traces.py
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---
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# Compare Configs
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Compare results between agent configurations to find signal about MCP tool impact.
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## Steps
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### 1. Run the comparison
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```bash
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cd ~/CodeContextBench && python3 scripts/compare_configs.py --format json
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```
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### 2. Present results as tables
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**Overall pass rates** by config, **divergence analysis** (stable, all-fail, divergent), and **divergent task detail table**.
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Focus on: biggest winner, MCP helps, MCP hurts, all-fail tasks.
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### 3. MCP-conditioned analysis (optional)
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```bash
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python3 scripts/mcp_audit.py --paired-only --json --verbose 2>/dev/null
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```
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Separates used-MCP vs zero-MCP tasks. Present reward delta table by intensity bucket.
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### Variants
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```bash
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python3 scripts/compare_configs.py --suite ccb_pytorch --format json
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python3 scripts/compare_configs.py --divergent-only --format json
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python3 scripts/compare_configs.py --format table
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```
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---
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# MCP Audit
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Analyze MCP (Sourcegraph) tool usage across benchmark runs.
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## What This Does
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`scripts/mcp_audit.py`:
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1. Collects `task_metrics.json` from paired_rerun batches
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2. Pairs baseline vs sourcegraph_full tasks
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3. Classifies by MCP usage: zero-MCP vs used-MCP (light/moderate/heavy)
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4. Computes reward and time deltas conditioned on actual MCP usage
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5. Identifies negative flips
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## Steps
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### 1. Run the audit
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```bash
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cd ~/CodeContextBench && python3 scripts/mcp_audit.py --json --verbose 2>/dev/null
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```
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### 2. Present key findings
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Tables: Overview, per-benchmark MCP adoption, reward deltas (used-MCP only), timing deltas.
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### 3. Investigate zero-MCP tasks
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Classify: trivially local, explicit file list, full local codebase, both configs failed, agent confusion.
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### 4. Check for negative flips
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Tasks where baseline passes but SG_full fails.
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### 5. MCP tool distribution
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Show which tools are most/least used.
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### 6. Summary and recommendations
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MCP value, MCP risk, optimization opportunities, cost-benefit.
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### Variants
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```bash
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python3 scripts/mcp_audit.py --all-runs --json --verbose
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python3 scripts/mcp_audit.py --verbose # text output
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```
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### Key Technical Notes
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- Transcript-first extraction: Tool counts from `claude-code.txt`, NOT `trajectory.json`
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- Paired reruns: BL and SF concurrent on same VM
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- MCP tool name variants: `sg_` prefix or not, script handles both
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---
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# IR Analysis
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Measure how well agents find the right files, comparing baseline vs MCP retrieval against ground truth.
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## Steps
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### 1. Ensure ground truth is built
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```bash
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cd ~/CodeContextBench && python3 scripts/ir_analysis.py --build-ground-truth
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```
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### 2. Run the IR analysis
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```bash
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cd ~/CodeContextBench && python3 scripts/ir_analysis.py --json 2>/dev/null
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```
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### 3. Present key findings
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Per-benchmark IR scores, overall aggregates, statistical tests.
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Key metrics: file recall, MRR, context efficiency, P@K.
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### Variants
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```bash
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python3 scripts/ir_analysis.py --per-task --json 2>/dev/null
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python3 scripts/ir_analysis.py --suite ccb_swebenchpro 2>/dev/null
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```
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### Ground Truth Sources
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| Benchmark | Strategy | Confidence |
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|-----------|----------|:----------:|
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| SWE-bench Pro | Patch headers | high |
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| PyTorch | Diff headers | high |
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| K8s Docs | Directory listing | high |
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| Governance/Enterprise | Test script paths | medium |
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| Others | Instruction regex | low |
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---
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# Cost Report
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Analyze token usage and estimated cost across benchmark runs.
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## Steps
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```bash
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cd ~/CodeContextBench && python3 scripts/cost_report.py
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```
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Shows: total cost/tokens/hours, per suite/config breakdown, config cost comparison, top 10 most expensive tasks.
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### Variants
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```bash
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python3 scripts/cost_report.py --suite ccb_pytorch
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python3 scripts/cost_report.py --config sourcegraph_full
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python3 scripts/cost_report.py --format json
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```
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---
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# Evaluate Traces
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Comprehensive evaluation of benchmark run traces: data integrity, output quality, efficiency analysis.
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## Phases
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### Phase 1: Scope Selection
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- MANIFEST: `runs/official/MANIFEST.json`
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- Audit script: `python3 scripts/audit_traces.py [--json] [--suite X] [--config X]`
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### Phase 2: Data Integrity
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- MCP adoption validation (transcript-first, check both `sg_` prefix variants)
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- Baseline contamination check (zero `mcp__sourcegraph` calls)
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- Infrastructure failure detection (zero-token, crash, null-token H3 bug)
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- Dedup integrity
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### Phase 3: Output Quality
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- Per-suite reward analysis
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- Cross-config comparison (matched tasks)
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- Task-level quality patterns (MCP helps/hurts/neutral)
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### Phase 4: Efficiency
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- Token usage and cost estimates
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- Wall clock time deltas
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- MCP tool distribution
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- Cost-effectiveness ratios
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### Phase 5: Synthesis
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Write report to `docs/TRACE_AUDIT_<date>.md`.
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## Known Patterns
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1. Zero-token (int 0) = auth failures
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2. Null-token + no trajectory + <=5 lines = crash failures
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3. Null-token + valid rewards = H3 token-logging bug (not failures)
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4. MCP distraction on TAC
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5. Deep Search unused (~1%)
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6. SWE-Perf regression under SG_base
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7. Subagent MCP calls hidden in trajectory.json (only in claude-code.txt)
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8. Zero-MCP is ~80% rational
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9. Monotonic MCP intensity-reward: Light +2.2%, Moderate +3.6%, Heavy +6.1%

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