autoflow

name	autoflow
description	Autoresearch orchestrator — discovers skills with autoresearch.enabled, runs the experiment loop, and uses a multiplicative composite metric with hard constraint gates.
disable-model-invocation	true
allowed-tools	Read, Write, Edit, Bash, Grep, Glob
argument-hint	<start|status|pause|resume|metrics|history>

Autoflow — Autoresearch Orchestrator for Skill Self-Improvement

Purpose

Discover skills that opt into the autoresearch pattern (via autoresearch.enabled: true in their manifest), run experiments autonomously, measure quality via a multiplicative composite metric, and trigger skill self-improvement with strict ratchet mechanics. This is Karpathy's autoresearch applied to software delivery: agents experiment on real tasks, measure results with fixed evaluation infrastructure, and upgrade their own skills — but only when the evidence confirms improvement.

How It Works

1. Configure — human picks an approval checkpoint (trust level) and reviews an execution plan with sequencing and parallelization rationale
2. Discover autoresearch-enabled skills by scanning manifest.yaml files for autoresearch.enabled: true
3. Load program.md (human control surface) for thresholds, constraints, and cadence
4. Plan — deep-inspect eligible issues, infer explicit and implicit dependencies, build serial waves and bounded parallel packets, then present the plan for approval
5. Run experiments: process approved runnable issues through the target skill with human gates auto-approved below the checkpoint and park issues at waiting-human checkpoints without stalling the whole drain
6. Measure with the multiplicative composite score: S = f^α · p^β · q^γ · (1-r)^δ
7. Improve after every completed issue when thresholds are breached: snapshot → skill-improve → evaluation window → ratchet decide
8. Keep or revert — binary decision, no ambiguity, hard constraint gates as vetoes

Approval Checkpoints

At session start, the human chooses a trust level that controls which human gates auto-approve:

• after-brainstorm / after-design / after-spec / after-scope / after-qa / after-pr / full-auto / all-gates

Default and max autonomy configured in program.md. Quality gates are never bypassed.

Inputs

• Subcommand: start, status, pause, resume, metrics, history

Steps

1. Follow the command specification in ${AGENTS_SKILLS_ROOT}/autoflow/commands/autoflow.md exactly.
2. Always read program.md from the repository root before starting any loop iteration.
3. Never modify program.md, _shared/*.py scripts, or _shared/autoresearch.md — these are fixed evaluation infrastructure.
4. Respect all escalation and cadence rules from program.md.
5. Log every run and improvement cycle to .jarvis/context/autoflow/ (per-project state).
6. Use ratchet.py for all score computation, gate checking, and keep/revert decisions.

Output

• Run-by-run execution log with ratchet scores
• Aggregate quality score trend (multiplicative composite)
• Hard constraint gate status
• Improvement history with keep/revert decisions and evidence
• Escalation notifications when thresholds or constraints are breached
• Pool state (when running concurrently)
• Approved execution plan with dependency and packet rationale

The Autoresearch Protocol

See ${AGENTS_SKILLS_ROOT}/_shared/autoresearch.md for the full protocol specification, including the three-file contract, metric layers, and adoption checklist.

Decision Trace Protocol

This skill participates in the skill evolution system by capturing decision traces at gate resolutions and consulting accumulated feedback.

Trace Consultation (short loop)

Before executing any step with a quality or human gate, query accumulated decision traces:

python3 "${AGENTS_SKILLS_ROOT}/_shared/trace_query.py" recent \
    --skill "autoflow" --gate "<gate_name>" --limit 5 --min-loops 1

If patterns or recent feedback exist, incorporate them as additional constraints. Do not cite traces to the user unless asked.

Trace Capture (after gate resolution)

After every gate resolves, capture a decision trace:

python3 "${AGENTS_SKILLS_ROOT}/_shared/trace_capture.py" capture \
    --skill "autoflow" \
    --gate "<gate_name>" --gate-type "<human|quality>" \
    --outcome "<approved|rejected|passed|failed>" \
    --refinement-loops <N> \
    [--feedback "<user's feedback text>"] \
    [--category <CATEGORY>]

Post-Run Retrospective

After completion, ask: "Any feedback on this autoflow run? (skip to finish)" If provided, capture via trace_capture.py with gate "run_retrospective" and gate-type "retrospective".