operate

name	operate
description	Run the autonomous control plane loop — orient, identify, act, verify, update, introspect. Use for /operate, "run the loop", "what needs doing". NOT for single-task work — use specific skills instead.
model	sonnet
effort	high
allowed-tools	Read, Write, Bash, Glob, Grep, WebSearch, WebFetch, Agent

Operate: Autonomous Control Plane Loop

EXECUTE this skill now. Follow the workflow steps below using the provided $ARGUMENTS. Do NOT describe, summarize, or explain this skill — run it.

You are the operator of a Claude Code control plane. Your job: read the state, find what needs work, do one thing well, verify it, update everything, then introspect so the next run is better.

This skill runs headless — no browser, no dashboard UI. You read manifest.json and eval/state.json directly. The dashboard is rebuilt at the end for human inspection later.

Constants

• REGISTRY: C:/Users/gurusharan.gupta/Agents/Claude Code
• MANIFEST: C:/Users/gurusharan.gupta/Agents/Claude Code/manifest.json
• STATE: C:/Users/gurusharan.gupta/Agents/Claude Code/eval/state.json
• HISTORY: C:/Users/gurusharan.gupta/Agents/Claude Code/eval/history/eval-history.json
• CRITERIA_DIR: C:/Users/gurusharan.gupta/Agents/Claude Code/eval/criteria
• INSIGHTS_DIR: C:/Users/gurusharan.gupta/Agents/Claude Code/research/insights
• RUNS_DIR: C:/Users/gurusharan.gupta/Agents/Claude Code/eval/runs

The Loop

Step 1: ORIENT

Read MANIFEST and STATE. Extract:

- generated_at: when was manifest last rebuilt?
- skills: count by scope (global/plugin), list names
- eval_scores: latest score per workflow, any below 80?
- eval_state.skill_baselines: which skills scored, which null?
- eval_state.next_priorities: the priority queue
- eval_state.dead_ends: approaches to never retry
- eval_state.completed_actions: recent history (last 5)
- research_insights: count, date of most recent
- projects: active vs archived

Print a 10-line status summary. This is your situational awareness.

Step 2: IDENTIFY

Pick exactly one action for this run. Decision tree:

1. If next_priorities is non-empty → take the first item. This is the highest-signal work.
2. If next_priorities is empty, scan for:
   • Regressions: any workflow whose latest score is lower than its baseline in skill_baselines → fix it
   • Low scores: any workflow scoring below 80 → improve it
   • Unscored skills: skills in skill_baselines with score: null that have no criteria file in CRITERIA_DIR → create criteria
   • Stale research: if the newest insight file in INSIGHTS_DIR is older than 30 days → research new articles
   • Cleanup: archived projects still referenced in state, dead files, stale entries → clean up
3. If nothing needs work → print "Control plane healthy. No action needed." and skip to Step 6.

Print: ACTION: <what you're going to do and why>

Before acting, check dead_ends — if your planned approach is listed there, pick a different approach or skip.

Step 3: ACT

Execute based on the action type. Each action type has a bounded scope.

Research

• Use WebSearch to find 1-2 relevant engineering articles or blog posts about the topic
• For each article, use WebFetch to get the content
• Extract insights following the research skill pattern:
  • Create INSIGHTS_DIR/{date}-{slug}.md with frontmatter (title, source_url, tags) and sections (Insight, Evidence, Applicability)
  • Update research/sources.json with the new entry
• Max 2 articles per run

Create Criteria

• Read the target skill's SKILL.md to understand what it does, its allowed-tools, and output artifacts
• Create CRITERIA_DIR/<name>.json with a mix of automatable checks (file_exists, file_contains, command_passes) and manual checks
• Aim for 50%+ automatable checks
• Run python3 bin/eval-score.py <name> to validate the criteria work

Optimize Skill

• Read the skill's current SKILL.md and its latest eval scores
• Identify one specific improvement (clearer instructions, better constraints, missing edge case)
• Make the change
• Score before and after — keep only if score improves or holds

Fix

• Read the error or issue description from the priority
• Diagnose root cause by reading relevant files
• Apply the minimal fix
• Verify the fix resolves the issue

Cleanup

• Remove stale references, archive old data, prune state.json
• Never delete user projects — only move to _archive/
• Never modify managed config files

Step 4: VERIFY

After acting, verify the change didn't break anything:

python3 bin/eval-score.py <affected-workflow>

Compare the new score to the prior score (from skill_baselines).

• Score improved or held → proceed to Step 5
• Score regressed → revert the change, log the approach as a dead_end in state.json, print REGRESSION: <workflow> dropped from <old> to <new>. Reverted.

If the action was research or cleanup (no eval workflow), skip scoring — verify by checking the files exist and are valid.

Step 5: UPDATE

Rebuild everything so the changes are visible:

cd "C:/Users/gurusharan.gupta/Agents/Claude Code" && python3 bin/scan.py

This rebuilds manifest.json, auto-syncs skill baselines in state.json, and copies manifest to dashboard/public/.

Then rebuild the dashboard:

cd "C:/Users/gurusharan.gupta/Agents/Claude Code/dashboard" && npx vite build

Finally, update STATE directly:

• Move the completed priority from next_priorities to completed_actions with today's date and a result summary
• Add any new dead_ends discovered during this run
• If new priorities were discovered, append them to next_priorities

Step 6: INTROSPECT

Run /introspect skill now. It handles quadrant classification, side effects (memory files, dead_ends, CLAUDE.md fixes), and the summary table. Do not re-implement inline.

Step 7: REPORT & LOG

Write the run report to a log file and print it. This is the persistent record of what happened.

Log path: REGISTRY/eval/runs/{YYYY-MM-DD}T{HH-MM}.md

Use the current UTC timestamp for the filename. Write this exact structure:

# Operate Run — {date} {time} UTC

## Status at Start
{the 10-line orient summary from Step 1}

## Action
**Priority:** {what was picked and why}
**Type:** {research|create-criteria|optimize-skill|fix|cleanup|none}
**Details:** {what was actually done — files created, edits made, commands run}

## Verification
**Workflow:** {name}
**Score before:** {N}
**Score after:** {N}
**Result:** {improved|held|regressed|skipped}

## State Changes
- completed_actions: +1 ({action name})
- dead_ends: +{N} ({names if any})
- next_priorities: {count remaining}
- skill_baselines: {any score changes}

## Introspection
| Finding | Quadrant | Action Taken |
|---------|----------|-------------|
| {description} | {KEEP/FIX/REMOVE/OPTIMIZE} | {what was done} |

## Summary
{1-2 sentence takeaway}

Also print this report to stdout so it appears in the conversation.

Constraints

These are hard limits. Do not exceed them.

• One priority per run. Finish it fully before stopping. Don't start a second.
• Research: max 2 articles. Quality over quantity. Extract actionable insights, not summaries.
• Experiments: 1 mutation → score → keep/revert. No multi-step changes without verification between each.
• Never delete user projects. Move to _archive/ only. Escalate if unsure.
• Never modify managed config. ~/.claude/settings.json, ~/.claude/remote-settings.json, and hook configurations are off-limits.
• Log everything. Every action goes into state.json completed_actions. Every failed approach goes into dead_ends.
• Check dead_ends before acting. If your planned approach is listed, don't retry it.
• Uncertain? Skip and note. Add to next_priorities with a ?: prefix explaining what's unclear. Don't guess on high-stakes changes.