Autoresearch Wrapper

Use this skill when the user wants Codex to scan a repo for optimization candidates, build a dependency graph for each part, lock a metric and run spec, and then run or resume an autoresearch-style loop without mutating the main checkout.

The helper CLI lives at:

python3 scripts/autoresearch_wrapper.py

Read references/karpathy-autoresearch.md only when you need the upstream reference shape or need to refresh the local reference clone.

Public interface

Treat these names as the skill's command surface:

• /autoresearch-wrapper
• /autoresearch-wrapper:status
• /autoresearch-wrapper:run
• /autoresearch-wrapper:flow
• /autoresearch-wrapper:create
• /autoresearch-wrapper:delete
• /autoresearch-wrapper:monitor

These map to CLI subcommands:

• /autoresearch-wrapper -> wizard, or wrap <script-path> when the user passes a repo-local script path
• /autoresearch-wrapper:status -> status
• /autoresearch-wrapper:run -> run
• /autoresearch-wrapper:flow -> flow
• /autoresearch-wrapper:create -> create
• /autoresearch-wrapper:delete -> delete
• /autoresearch-wrapper:monitor -> monitor

Core rules

• Use Git worktrees as much as possible.
• Never optimize in the primary checkout if a worktree can be used.
• Treat the optimization unit as a module/file by default.
• Build and use a direct dependency graph for each part during scan.
• Mark a part as surely optimizable only when the target, candidate space, and metric are all explicit.
• Mark a part as probably optimizable when any of those are unclear, especially the metric or important direct dependencies.
• If the metric is unknown, do not run. Ask the user to define or confirm it first.
• Persist runtime state in .autoresearch-wrapper/state.json and human-readable status in .autoresearch-wrapper/STATUS.md.
• Regenerate a planning workspace under .autoresearch-wrapper/plans that mirrors part paths and initializes per-part planning files.

Workflow

/autoresearch-wrapper

If invoked with no arguments or just /autoresearch-wrapper, route to the end-to-end wizard:

1. Run:

python3 scripts/autoresearch_wrapper.py wizard

If you are operating without a real interactive stdin and the CLI cannot complete the prompts directly, emulate the same flow manually:

• Run python3 scripts/autoresearch_wrapper.py scan --no-interactive
• Start from the compact core-functionality summary and focused dependency graph
• Only ask for the full language/directory listing if the user explicitly wants a broader scan
• Ask which functionality area they want to improve first
• Ask which specific part to optimize
• Ask for the metric name, metric command, metric goal
• Ask for execution mode (sequential / parallel / wild) and rounds
• Persist the configuration with configure
• Ask if the user wants to start the run immediately

2. Let the wizard:
   • scan the repo and print the compact core-functionality summary
   • propose functionality-oriented slices such as best-defined targets, latency-sensitive paths, or dependency hubs
   • select the target part
   • lock the metric name, command, goal, and execution mode
   • persist the config
   • optionally start the run immediately

If the user explicitly wants the full scan listing first, run:

python3 scripts/autoresearch_wrapper.py wizard --full-summary

If the user provides a repo-local script path directly, use the script-wrapper shortcut:

python3 scripts/autoresearch_wrapper.py wrap path/to/script.py

This should:

• select that script as the primary part
• keep the normal dependency-aware scan/state model
• infer a metric preset and suggested metric command
• create an incomplete config stub until the metric command is confirmed

/autoresearch-wrapper:status

Run:

python3 scripts/autoresearch_wrapper.py status

Report:

• discovered parts
• each part's status
• dependency table and key neighbors
• readiness of the selected part
• active run id
• candidate worktrees and their lifecycle
• compact recorded metric-flow summaries

/autoresearch-wrapper:run

Run:

python3 scripts/autoresearch_wrapper.py run

Behavior:

• refuse to run if any required config field is missing
• create or resume the active run
• generate a Karpathy-style per-run program.md
• initialize a seed worktree and additional candidate worktrees for parallel or wild mode

The generated run program will point back to the helper CLI for:

• allocate
• evaluate
• record

/autoresearch-wrapper:flow

Run:

python3 scripts/autoresearch_wrapper.py flow

Report:

• recorded metric sequence for the selected run
• best-so-far sequence
• tabular step history
• ASCII plot of metric values

/autoresearch-wrapper:create

Run:

python3 scripts/autoresearch_wrapper.py create --part <part> --feature "<description>" --candidates <n> --metric <metric> --metric-command "<cmd>" --metric-goal <minimize|maximize>

Behavior:

• identify affected parts via the dependency graph
• create N candidate worktrees, each for a different implementation approach
• generate a per-run program.md with comparison instructions
• use evaluate and record against each approach to find the best capability ceiling

/autoresearch-wrapper:delete

Run:

python3 scripts/autoresearch_wrapper.py delete --part <part> --metric <metric> --metric-command "<cmd>" --metric-goal <minimize|maximize>

Behavior:

• identify transitive dependents of the deleted part
• create a seed worktree with the target file removed
• optimize dependent parameters in subsequent candidate worktrees
• generate a per-run program.md with post-deletion optimization instructions

/autoresearch-wrapper:monitor

Run:

python3 scripts/autoresearch_wrapper.py monitor --interval <seconds>

Behavior:

• poll the active run at the configured interval
• report rounds completed, best metric, early exit status
• exit when the run completes or Ctrl-C is pressed

Helper commands

Use these directly when managing a run:

python3 scripts/autoresearch_wrapper.py allocate --run-id <run>
python3 scripts/autoresearch_wrapper.py evaluate --run-id <run> --candidate <seed|candidate-001>
python3 scripts/autoresearch_wrapper.py record --run-id <run> --candidate <id> --status auto --description "<summary>"

Use metric presets to scaffold or confirm a script-based metric command:

python3 scripts/autoresearch_wrapper.py configure --part path/to/script.py --metric-preset runtime_seconds --use-suggested-command
python3 scripts/autoresearch_wrapper.py preset-metric --preset runtime_seconds --script path/to/script.py

Use reference to clone or refresh the upstream repo locally:

python3 scripts/autoresearch_wrapper.py reference
python3 scripts/autoresearch_wrapper.py reference --refresh

Use resources to detect system resources and set concurrency defaults:

python3 scripts/autoresearch_wrapper.py resources

Use early exit to stop runs that stall:

python3 scripts/autoresearch_wrapper.py configure --part <part> --early-exit-patience 3 --early-exit-threshold 0.01

Use wild mode for multi-parameter search when the search space is large:

python3 scripts/autoresearch_wrapper.py configure --part <part> --mode wild --wild-max-simultaneous 3

Expected agent behavior inside a run

• Evaluate the baseline seed candidate first.
• Keep or discard later candidates strictly from the configured metric.
• Preserve all candidate history in the run directory.
• Reuse the persisted active run when the user asks to resume instead of starting a fresh run.