| name | aperture_lab_autoresearch |
| description | Run Aperture Lab F-Stop on imported public trajectories, prepare review artifacts, compare disagreements, and propose bounded semantic-layer improvements under strict replay gates. |
| metadata | {"openclaw":{"requires":{"bins":["pnpm"]},"os":["linux","darwin"]}} |
Aperture Lab F-Stop
Use this skill when the task is to run Aperture's offline semantic
improvement loop on a remote worker or long-running harness.
This skill is only for the Lab path.
Do not use it for live runtime behavior or product-surface work.
Load First
Before doing anything else, read:
packages/lab/research/autoresearch-program.mdpackages/lab/research/autoresearch-config.json
Those files are the source of truth for:
- allowed edit paths
- forbidden edit paths
- evaluation commands
- artifact chain
- expected outcomes
- non-goals
Follow them strictly.
Main Rule
Keep AI out of the hot path.
This loop may:
- import public trajectories
- prepare offline review artifacts
- run reviewer responses into disagreements and recommendation summaries
- promote selected disagreements into a frozen calibration corpus
- evaluate that corpus repeatably
- propose bounded semantic/importer changes
- run replay and release gates
This loop must not:
- change the live decision path to depend on AI
- edit planner or continuity logic
- auto-merge
Core Commands
Use the provider-neutral lab:fstop:* surface as the default operating path:
pnpm lab:fstop:run --provider <provider> --reviewer-provider <provider> --optimizer-provider <provider> --json
APERTURE_OPENCLAW_REVIEW_TIMEOUT=60 pnpm lab:fstop:review --dataset swe-smith --split tool --limit 3 --reviewer-provider <provider> --json
pnpm lab:fstop:propose --reviewer-provider <provider> --optimizer-provider <provider> --json
pnpm lab:fstop:cycle --json
pnpm lab:fstop:optimize --provider <provider> --json
pnpm judgment:battle
pnpm release:check
For the default short OpenClaw agent-run command, use:
pnpm lab:fstop:openclaw
If you need to debug one bundle manually, fall back to:
pnpm trajectory:import --dataset swe-smith --split tool --limit 3
pnpm lab:fstop:prepare --bundle <bundle-path> --json
pnpm lab:fstop:review:run --artifact <artifact-path> --reviewer-command "pnpm lab:fstop:reviewer --provider <provider>" --json
To freeze reviewer-backed disagreements into the repeatable optimization
surface, use:
pnpm lab:fstop:promote --report <report-path> --split train --json
pnpm lab:fstop:evaluate --json
pnpm lab:fstop:cycle --json
Use the discovery loop to find candidate problems.
Use the frozen calibration loop to judge whether a patch actually helped.
The canonical reviewer adapter is:
pnpm lab:fstop:reviewer --provider <provider>
Supported providers:
The adapter resolves the actual provider command from:
APERTURE_HERMES_REVIEWER_COMMANDAPERTURE_OPENCLAW_REVIEWER_COMMANDAPERTURE_REVIEWER_COMMAND
For OpenClaw, the adapter can also invoke the local openclaw binary directly
when no override command is configured. Use these env vars to tune that path:
APERTURE_OPENCLAW_BINAPERTURE_OPENCLAW_AGENTAPERTURE_OPENCLAW_REVIEW_SESSION_IDAPERTURE_OPENCLAW_REVIEW_THINKINGAPERTURE_OPENCLAW_REVIEW_TIMEOUT
By default it uses a fresh OpenClaw session id per review and avoids the shared
main agent session unless APERTURE_OPENCLAW_AGENT is explicitly set.
The underlying reviewer command must:
- read the reviewer prompt on stdin
- write valid JSON to stdout
- exit non-zero on failure
If the provider supports skills, use the dedicated reviewer role:
skills/aperture-lab-reviewer/SKILL.md
For the code-editing phase, use the dedicated optimizer role:
skills/aperture-lab-optimizer/SKILL.md
The canonical optimizer adapter is:
pnpm lab:fstop:optimizer --provider <provider>
It resolves provider-specific optimizer commands from:
APERTURE_HERMES_OPTIMIZER_COMMANDAPERTURE_OPENCLAW_OPTIMIZER_COMMANDAPERTURE_OPTIMIZER_COMMAND
For OpenClaw, the adapter can also invoke the local openclaw binary directly
when no override command is configured. Use these env vars to tune that path:
APERTURE_OPENCLAW_BINAPERTURE_OPENCLAW_OPTIMIZER_AGENTAPERTURE_OPENCLAW_OPTIMIZER_SESSION_IDAPERTURE_OPENCLAW_OPTIMIZER_THINKINGAPERTURE_OPENCLAW_OPTIMIZER_TIMEOUT
The unattended optimizer entrypoint is:
pnpm lab:fstop:optimize --provider <provider> --json
It should be run from a clean worktree.
The unattended proposal entrypoint is:
pnpm lab:fstop:propose --reviewer-provider <provider> --optimizer-provider <provider> --json
Prefer this when the worker should go all the way from:
- discovery batch
- to repeated-signal selection
- to candidate calibration promotion
- to a reviewable patch proposal
The unattended top-level runner entrypoint is:
pnpm lab:fstop:run --provider <provider> --reviewer-provider <provider> --optimizer-provider <provider> --json
Prefer this when the provider should manage repeated proposal slices on its own
instead of waiting for manual slice selection.
Output Expectations
Good runs should produce:
- review artifacts under
packages/lab/results/offline-review/requests
- reviewer-filled artifacts under
packages/lab/results/offline-review/responses
- reviewer prompts under
packages/lab/results/offline-review/prompts
- raw reviewer outputs under
packages/lab/results/offline-review/raw
- disagreement reports under
packages/lab/results/offline-review/disagreements
- recommendation summaries under
packages/lab/results/offline-review/recommendations
- run summaries under
packages/lab/results/offline-review/runs
- calibration cases under
packages/lab/calibration
- calibration reports under
packages/lab/results/autoresearch/evaluations
- optimization briefs under
packages/lab/results/autoresearch/briefs
- optimizer prompts, raw outputs, patches, and run summaries under
packages/lab/results/autoresearch/optimizer
- proposal artifacts under
packages/lab/results/autoresearch/proposals
- candidate bounded code changes only on the allowed edit surface
- a clear pass/fail result from the gates
Artifact Chain
Execute the loop in this order:
- input bundle
- Aperture replay output
- prepared review artifact
- reviewer prompt
- raw reviewer output
- reviewer-filled artifact
- disagreement report
- recommendation summary
- promoted calibration case
- frozen calibration evaluation report
- optimization brief
- optimizer prompt
- raw optimizer output
- proposal artifact
- optional bounded patch proposal
- gated evaluation result
Optimization Target
Optimize for:
- better title extraction
- better summary extraction
- better semantic frame reads
- better tool-family reads
- better consequence-band reads
Do not optimize for:
- general product UX
- continuity behavior
- route churn for its own sake
When In Doubt
Prefer:
- narrower edits
- stronger replay safety
- frozen calibration evidence over one-off reviewer disagreements
- better disagreement artifacts
over:
- aggressive autonomous rewriting
- speculative planner changes
- touching unrelated repo areas
