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pi-skill/skills/autoresearch/SKILL.md
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---
name: autoresearch
description: Autonomous Goal-directed Iteration. Apply Karpathy's autoresearch principles to ANY task. Loops autonomously — modify, verify, keep/discard, repeat. Invoke with /skill:autoresearch or when user says "work autonomously", "iterate until done", "keep improving", or "run overnight".
allowed-tools: Bash(git:*) Bash(npm:*) Bash(npx:*) Read Write Edit ask_user show_plan show_research subagent_create_batch dispatch_agent commander_task commander_mailbox show_report
---
# Autoresearch — Autonomous Goal-directed Iteration
Inspired by [Karpathy's autoresearch](https://github.com/karpathy/autoresearch). Applies constraint-driven autonomous iteration to ANY work — not just ML research.
**Core idea:** You are an autonomous agent. Modify -> Verify -> Keep/Discard -> Repeat.
## When to Activate
- User invokes `/skill:autoresearch` or `/autoresearch`
- User says "work autonomously", "iterate until done", "keep improving", "run overnight"
- Any task requiring repeated iteration cycles with measurable outcomes
## Phase 1: Understand (Do This First — Before ANY Work)
Before touching any files, deeply understand the goal. Do NOT rush into iteration.
1. **Read relevant files** — Scan the codebase to build context around the user's goal. Understand what exists, what patterns are in use, and what's realistic.
2. **Identify ambiguities** — Based on the goal and codebase context, what's unclear?
- Is the success metric obvious or ambiguous?
- Is the scope (which files to modify) clear?
- Are there constraints the user hasn't mentioned?
- Are there multiple valid interpretations?
3. **Ask clarifying questions** — If ANY ambiguity exists, use `ask_user` to ask targeted questions:
```
ask_user {
question: "I have a few questions before I build the research plan:",
mode: "questions",
options: [
{ label: "1. What metric should define success? (e.g. test coverage %, build time ms, bundle size KB)" },
{ label: "2. Which files/directories are in scope for modification?" },
{ label: "3. Are there any approaches to avoid or constraints I should know about?" },
{ label: "4. What does 'done' look like — a specific target, or iterate until interrupted?" }
]
}
```
**Tailor questions to the specific goal.** Don't ask about what's already clear. Ask about genuine ambiguities.
4. **Skip if crystal clear** — If the goal is unambiguous (clear metric, scope, exit criteria), skip questions and proceed to Phase 2. State briefly why no questions are needed.
5. **Synthesize understanding** — Form a concrete statement: Goal, Metric (what + direction + verify command), Scope (in/out), Constraints, Exit criteria.
6. **Save research session** — Create `.context/research-sessions/<session-id>.json` with the initial session data: goal, metric, scope, clarifying Q&A, status "understanding". Store the `session_id` for updates throughout the lifecycle.
## Phase 2: Plan (Present Before Executing)
Now write and present a research plan for user approval. Do NOT start iterating without approval.
1. **Establish baseline** — Run the verification command to get a starting metric value.
2. **Write the research plan** — Create `.context/autoresearch-plan.md`:
```markdown
# Autoresearch Plan: <goal summary>
## Goal
<Concrete goal statement>
## Metric
- **Measuring:** <what>
- **Direction:** <higher/lower is better>
- **Verify command:** `<command>`
- **Baseline:** <current value>
- **Target:** <target value or "continuous improvement">
## Scope
- **In scope:** <files/directories to modify>
- **Read only:** <files for context only>
- **Out of scope:** <excluded areas>
## Strategy
Ordered approaches, most to least promising:
1. <First approach — why promising>
2. <Second approach — what it explores>
3. <Third approach — alternative angle>
4. <Fourth approach — radical idea>
5. <Fifth approach — simplification play>
## Iteration Plan
- **Mode:** <bounded (N) / unbounded>
- **Estimated time per iteration:** <seconds/minutes>
- **When stuck:** Re-read plan, combine near-misses, try opposites
## Exit Criteria
- <When to stop>
```
3. **Present for approval:**
```
show_plan { file_path: ".context/autoresearch-plan.md", title: "Autoresearch Plan: <goal>" }
```
- **Approved** → proceed to Phase 3
- **Declined** → revise based on feedback and re-present
4. **Update session** — Set status to "planning", save plan content and baseline metric.
## Phase 3: Setup & Begin
With understanding confirmed and plan approved, set up tracking and start.
1. **Create results log** — Create `autoresearch-results.tsv` (see `references/results-logging.md`)
2. **Record baseline** — Log the baseline metric from Phase 2 as iteration #0
3. **Commander tracking** — If available, create task group and broadcast start (see Commander Integration below)
4. **Update session** — Set status to "researching"
5. **Begin the loop** — Start iterating immediately. No further confirmation needed.
## The Loop
Read `references/autonomous-loop-protocol.md` for full protocol details.
```
LOOP (FOREVER or N times):
1. Review: Read current state + git history + results log
2. Ideate: Pick next change based on goal, past results, what hasn't been tried
3. Modify: Make ONE focused change to in-scope files
4. Commit: Git commit the change (before verification)
5. Verify: Run the mechanical metric (tests, build, benchmark, etc.)
6. Decide:
- IMPROVED -> Keep commit, log "keep", advance
- SAME/WORSE -> Git revert, log "discard"
- CRASHED -> Try to fix (max 3 attempts), else log "crash" and move on
7. Log: Record result in results log
8. Repeat: Go to step 1.
- If unbounded: NEVER STOP. NEVER ASK "should I continue?"
- If bounded (N): Stop after N iterations, print final summary
```
## Critical Rules
1. **Loop until done** — Unbounded: loop until interrupted. Bounded: loop N times then summarize.
2. **Read before write** — Always understand full context before modifying
3. **One change per iteration** — Atomic changes. If it breaks, you know exactly why
4. **Mechanical verification only** — No subjective "looks good". Use metrics
5. **Automatic rollback** — Failed changes revert instantly. No debates
6. **Simplicity wins** — Equal results + less code = KEEP. Tiny improvement + ugly complexity = DISCARD
7. **Git is memory** — Every kept change committed. Agent reads history to learn patterns
8. **When stuck, think harder** — Re-read files, re-read goal AND `.context/autoresearch-plan.md` for planned strategy, try next untried approach from the plan, combine near-misses, try radical changes. Don't ask for help unless truly blocked by missing access/permissions
## Principles Reference
See `references/core-principles.md` for the 7 generalizable principles from autoresearch.
## Commander Integration (Task Tracking & Visibility)
When Commander is available, autoresearch MUST track every iteration as a Commander task. This gives the dashboard full visibility into autonomous work — just like the `tasks` extension does for manual workflows.
### Setup Phase (Phase 3) — Create Task Group
After establishing the baseline and getting plan approval, create a Commander task group for this research session:
```
commander_task {
operation: "group:create",
group_name: "Autoresearch: <goal summary>",
initiative_summary: "<full goal description with metric and scope>",
total_waves: 1,
working_directory: "<cwd>",
tasks: []
}
```
Store the returned `group_id` — all iteration tasks will be added to this group.
Send an initial mailbox status broadcast:
```
commander_mailbox {
operation: "send",
from_agent: "autoresearch",
to_agent: "commander",
body: "Autoresearch started: <goal>. Baseline metric: <value>. Scope: <files>. Plan approved.",
message_type: "status"
}
```
### Per-Iteration — Create → Claim → Complete
**Before modifying** (step 3 of each loop iteration), create and claim a Commander task:
```
commander_task { operation: "create", description: "Iteration #N: <planned change>", working_directory: "<cwd>", group_id: <group_id> }
commander_task { operation: "claim", task_id: <task_id>, agent_name: "autoresearch" }
```
**After logging results** (step 7 of each loop iteration), complete the task with the outcome:
```
commander_task { operation: "complete", task_id: <task_id>, result: "<status>: <description>. Metric: <old> → <new> (delta: <delta>)" }
```
Also add a comment to the task with detailed results:
```
commander_task { operation: "comment:add", task_id: <task_id>, body: "Status: <keep|discard|crash>\nMetric: <value> (delta: <delta>)\nCommit: <hash or '-'>\nDescription: <what was tried>", agent_name: "autoresearch" }
```
**Note:** Use `complete` for ALL outcomes (keep, discard, crash). Discards and crashes are expected in autoresearch — they're not failures. Reserve `fail` only for unrecoverable errors that halt the entire loop.
### Status Broadcasts — Every ~5 Iterations
Every 5 iterations, send a mailbox status update AND add a comment to the group:
```
commander_mailbox {
operation: "send",
from_agent: "autoresearch",
to_agent: "commander",
body: "Autoresearch progress — Iteration #N: metric at <value> (baseline: <baseline>). Keeps: X | Discards: Y | Crashes: Z",
message_type: "status"
}
```
### Research Complete — Report & Implementation Handoff (MANDATORY)
When the loop ends (bounded mode reaching N, or goal achieved):
1. **Final mailbox broadcast** with full summary:
```
commander_mailbox {
operation: "send",
from_agent: "autoresearch",
to_agent: "commander",
body: "Autoresearch complete (N iterations). Baseline: <X> → Final: <Y> (delta: <Z>). Keeps: A | Discards: B | Crashes: C. Best iteration: #M — <description>",
message_type: "result"
}
```
2. **Compile findings & next steps** — Extract prioritized, actionable implementation items from the research. Update the session file with findings, next steps array, and final metric.
3. **Research report** — Present via `show_report` framed as a handoff:
```
show_report {
title: "Research Complete — Ready for Implementation: <goal>",
summary: "## Research Results\n\n...\n\n## Prioritized Next Steps\n\n1. <action item>\n2. ...\n\n## Recommended Implementation Approach\n\n<how to implement>"
}
```
4. **Ask about implementation** — Use `ask_user` to offer three choices:
- **Implement now** → spawn a team of builder agents via `subagent_create_batch`
- **Save & pause** → set session to "paused", resume later via `/research`
- **Done** → mark session "complete"
5. **Implementation (if chosen)** — Update session to "implementing", create Commander task group, dispatch builders, track completion. When done, present final comprehensive report covering research results AND implementation work. Set session to "complete".
6. **Preserve the plan** — Leave `.context/autoresearch-plan.md` intact. Leave the session file for browsing via `/research`.
### Graceful Degradation
All Commander calls are **optional**. If Commander is unavailable:
- Skip `commander_task` and `commander_mailbox` calls silently
- The local `autoresearch-results.tsv` log remains the primary record
- The `show_report` call still works (it only needs git, not Commander)
- Never let a Commander error interrupt the autonomous loop
## Adapting to Different Domains
| Domain | Metric | Scope | Verify Command |
|--------|--------|-------|----------------|
| Backend code | Tests pass + coverage % | `src/**/*.ts` | `npm test` |
| Frontend UI | Lighthouse score | `src/components/**` | `npx lighthouse` |
| ML training | val_bpb / loss | `train.py` | `uv run train.py` |
| Blog/content | Word count + readability | `content/*.md` | Custom script |
| Performance | Benchmark time (ms) | Target files | `npm run bench` |
| Refactoring | Tests pass + LOC reduced | Target module | `npm test && wc -l` |
Adapt the loop to your domain. The PRINCIPLES are universal; the METRICS are domain-specific.
## Session Persistence
Every autoresearch session is saved to `.context/research-sessions/<session-id>.json`. This enables:
- **Resume later** — pick up where you left off via `/research` command
- **Browse history** — see all past research sessions in the research browser
- **Track lifecycle** — from understanding through implementation completion
Update the session file at every major transition: understand → plan → research → implement → complete. On every "keep" iteration or every ~5 iterations, append iteration data to the session. This creates a complete record of the research lifecycle that can be browsed and resumed.