recursive-training

A workflow for learning from completed recursive-mode runs by storing reusable guidance in the repository’s memory files.

In plain words
What is it for?
Use it after runs are locked to extract lessons, refresh repository memory, or load relevant prior guidance before starting new work.
Why use it?
It turns repeated successes and failures into local instructions that can improve later runs without changing the model itself.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/try-works/recursive-mode/recursive-training
Any agent
npx skills add try-works/recursive-mode --skill recursive-training
Clone the repo
git clone --depth 1 https://github.com/try-works/recursive-mode

Made for: Claude Code, Codex.

Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,636 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00034 $0.01636
Opus 5 $0.00017 $0.00818
Sonnet 5 $0.00007 $0.00327
Haiku 4.5 $0.00003 $0.00164

Measured 2d ago against content hash f6a9016c7da4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

recursive-training scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/recursive-training/SKILL.md · 196 lines

How it starts

The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 196 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 196 lines · 34 tokens per session scan A f6a9016c7da4

Subscribe to this mod's changes

recursive-training is a skill published in the GitHub repository try-works/recursive-mode (129 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,636 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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