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recursive-training

Use after completed recursive-mode runs accumulate to extract durable experiential memory into `/.recursive/memory/`, then load it for later runs through the canonical loader.

日本語の概要は準備中です。原文の説明を表示しています。

try-works/role-modelスター 117(GitHub 117 + cccskills 0)

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含まれるファイル(4)

  • SKILL.md6.7 KB
  • agents/openai.yaml270 B
  • references/memory-architecture.md3.6 KB
  • references/phase8-and-loading.md3.1 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

recursive-training

Purpose

Use this skill after a repository has accumulated completed recursive-mode runs and you want to turn repeated successes or failures into durable, repo-local guidance.

The canonical workflow still lives in /.recursive/RECURSIVE.md. This skill owns only the training, loading, and memory-discipline layer that sits around completed runs.

When to use it

Use recursive-training to:

  • extract cross-run learnings from completed recursive-mode runs
  • keep those learnings in /.recursive/memory/ instead of ad hoc mirrors
  • refresh memory after Phase 8 locks
  • load only the most relevant prior learnings before a new run starts
  • provide startup guidance without mutating the memory plane

Hard rules

  1. Repository-local only. Training data and extracted memory stay inside the current repo.
  2. No parameter updates. Learning happens through files in /.recursive/memory/, not model mutation.
  3. /.recursive/memory/ is the only canonical store. Pointer files are bootstrap-managed and non-authoritative.
  4. All markdown under /.recursive/run/<run-id>/ is eligible training input, not just 00-08.
  5. Group runs by subsystem only. The extractor assigns task types per learning item.
  6. Use contrastive extraction when both winners and losers exist; fall back to winner-only extraction for high-quality repos.
  7. Every extracted item must remain evidence-grounded in completed runs.
  8. Training scripts do not own AGENTS.md; bootstrap owns bridge-file updates.

Training model

recursive-training combines two ideas:

  • ReasoningBank-style memory items for structured, reusable extracted guidance
  • Training-free GRPO-style comparison for contrastive winner/loser extraction when variance exists

At a high level:

  1. Parse all markdown artifacts from completed runs.
  2. Infer the dominant subsystem from changed paths and evidence across those artifacts.
  3. Classify each subsystem group as contrastive, winner-only, or insufficient.
  4. Ask the extractor for structured learning items.
  5. Write those items into:
    • /.recursive/memory/domains/<subsystem>.md
    • /.recursive/memory/training/<task-type>.md
  6. Refresh the memory registry/startup guidance without treating pointer files as authoritative memory.

For the full schema, grouping logic, evidence signals, and extraction contracts, see:

  • references/memory-architecture.md
  • references/phase8-and-loading.md

Phase 8 and loading boundary

Phase 8 records the current run's observations in 08-memory-impact.md.

Training then turns many completed runs into cross-run memory.

That means:

  • 08-memory-impact.md is run-local capture
  • recursive-training-phase8-trigger.py is the handoff after Phase 8 locks
  • recursive-training-grpo.py performs extraction
  • recursive-training-loader.py is the canonical retrieval path before later runs
  • recursive-training-sync.py prints startup guidance without mutating the memory plane

Detailed Phase 8 handoff and loader behavior lives in references/phase8-and-loading.md.

Commands

Full training:

python .recursive/scripts/recursive-training-grpo.py --repo-root .

Incremental training after a specific run:

python .recursive/scripts/recursive-training-grpo.py --repo-root . --incremental --run-id <run-id>

Post-Phase 8 trigger:

python .recursive/scripts/recursive-training-phase8-trigger.py --repo-root . --run-id <run-id>
python .recursive/scripts/recursive-training-phase8-trigger.py --repo-root . --run-id <run-id> --auto

Read-only startup guidance:

python .recursive/scripts/recursive-training-sync.py --repo-root .

Canonical memory loading before a new run:

python .recursive/scripts/recursive-training-loader.py \
  --repo-root . \
  --query "<task description>" \
  --files "<comma-separated paths>"

Optional MCP convenience layer:

python .recursive/scripts/recursive-training-mcp.py --repo-root .

Trigger patterns

Recognize these as training requests:

  • train
  • training
  • extract memories
  • learn from runs
  • train from the latest run
  • incremental training
  • sync memories
  • /recursive-training

Default behavior:

  • fewer than 2 completed runs: explain why extraction is skipped
  • no explicit scope: default to full training
  • explicit run or "incremental": use incremental mode
  • "sync" or "what should I read": use recursive-training-sync.py

Operator checklist

  1. Confirm the repo already has recursive-mode scaffolding.
  2. Confirm completed runs exist under /.recursive/run/.
  3. Run the trigger or grpo script with the intended scope.
  4. Verify updated items land under /.recursive/memory/domains/ and /.recursive/memory/training/.
  5. Before the next run, read /.recursive/memory/MEMORY.md and call the loader with task context.
  6. Treat loader output as advisory context; the canonical records stay in the memory plane.

Script surface

  • .recursive/scripts/recursive-training-grpo.py
  • .recursive/scripts/recursive-training-grpo.ps1
  • .recursive/scripts/recursive-training-phase8-trigger.py
  • .recursive/scripts/recursive-training-phase8-trigger.ps1
  • .recursive/scripts/recursive-training-sync.py
  • .recursive/scripts/recursive-training-sync.ps1
  • .recursive/scripts/recursive-training-loader.py
  • .recursive/scripts/recursive-training-loader.ps1
  • .recursive/scripts/recursive-training-mcp.py
  • .recursive/scripts/recursive-training-mcp.ps1

Detailed references

  • references/memory-architecture.md — schema, evidence signals, grouping, extraction modes, and output contract
  • references/phase8-and-loading.md — Phase 8 handoff, trigger behavior, loader timing, and integration patterns

Coverage Gate

  • Canonical memory-store boundary documented
  • Contrastive and winner-only extraction modes documented
  • Subsystem-only grouping documented
  • All-markdown run-folder input scope documented
  • Phase 8 handoff and loader boundary documented
  • Canonical command surface documented
  • Reference docs linked for detailed extraction and loading behavior
  • Script-only transport boundary preserved

Coverage: PASS

Approval Gate

  • Skill defers to /.recursive/RECURSIVE.md for overall workflow rules
  • Repository-local scope preserved
  • No parameter-updating behavior introduced
  • /.recursive/memory/ remains the only canonical store
  • Phase 8 capture vs training extraction boundary is explicit
  • Loader remains the canonical retrieval path
  • Pointer files remain bootstrap-managed, not sync-authored mirrors

Approval: PASS

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