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loop

/loop

Orchestrate a goal through subagent iterations. Use when the user gives a goal to run "the loop" on — decomposes the goal, dispatches one subagent per iteration with fresh context, verifies each result before memory is committed.

Licence
MIT
Last repository change
30.09.2026

Assessment and security

100/ 100Class: good
Format25 / 25
Frontmatter with name and description, valid name, description up to 1,024 characters, name matching the folder.
Discoverability25 / 25
Description explains when to use the skill, has a reasonable length and uses third person.
Content35 / 35
At least 150 words and at most 500 lines, headings, step lists, examples or code.
Hygiene15 / 15
No placeholders such as TODO, no Windows paths, with a licence.
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Deprecation

No signals detected. Repository changed on 30.09.2026, without references to retired models or APIs.

Licence

MIT, open: use and modify while retaining author attribution.

What it does and when to use it

Orchestrate a goal through subagent iterations. Use when the user gives a goal to run "the loop" on — decomposes the goal, dispatches one subagent per iteration with fresh context, verifies each result before memory is committed.

Triggers from the description

the loop
Original author description

Orchestrate a goal through subagent iterations. Use when the user gives a goal to run "the loop" on — decomposes the goal, dispatches one subagent per iteration with fresh context, verifies each result before memory is committed.

How it works

  1. Why this shape
  2. Steps
  3. Rules
Original SKILL.md headings · 77 lines, 556 words
SKILL.md preview
---
name: loop
description: Orchestrate a goal through subagent iterations. Use when the user gives a goal to run "the loop" on — decomposes the goal, dispatches one subagent per iteration with fresh context, verifies each result before memory is committed.
---

# Loop

One job: drive a goal to done through iterations, keeping the human in the
gate. You (the main session) NEVER do the work yourself — you orchestrate,
verify, and commit. Subagents do the work, one iteration each, each with a
fresh context.

## Why this shape

- Each subagent starts clean — no confusion carryover between iterations
  (the fresh-context property that makes loops reliable).
- Your context stays small: goal, iteration summaries, verdicts. You can
  run many iterations in one sitting without rotting your own window.
- You verify every iteration BEFORE its memory writes are committed —
  self-improvement only compounds on verified work.

## Steps

1. Use the `session-start` skill yourself (load MEMORY.md, latest daily +
   reflection) so you can decompose and judge with context.

2. Break the goal into iteration-sized tasks: each completable by one agent
   in one run, with a pass/fail check you can state up front. Show the user
   the task list. Adjust if they object.

3. For each task, dispatch ONE fresh-context subagent — the Agent tool, general-purpose (Claude Code), or the `subagent` tool with
   `agent: "worker"` and `context: "fresh"` (pi) —
   with this brief:

   ```
   You are one iteration of an agent loop in <repo path>.
   1. Read vault/MEMORY.md, the latest note in vault/Daily/, and the latest
      note in vault/Reflections/. Apply the reflection's lesson.
   2. Do exactly this task: <task>. Done means: <pass/fail criteria>.
   3. Follow skills/capture/SKILL.md to save any durable facts to
      vault/Knowledge/ (update existing notes over creating duplicates).
   4. Follow skills/reflect/SKILL.md steps 1-3 to log the session and write
      a reflection — but do NOT git commit (the orchestrator commits).
   5. Return: what you did, what you captured, outcome
      (done/partial/blocked), and your one-sentence lesson.
   ```

   Run iterations SEQUENTIALLY — subagents write shared memory files
   (daily note, MEMORY.md). Parallel iterations corrupt them. Parallel is
   fine only for read-only research subagents that write nothing.

4. Verify the iteration: check the task's pass/fail criteria yourself
   (read the output, run the check, inspect `git -C <base> diff` of the vault —
   `<base>` = `cat ~/.config/loop-and-gate/vault`, or the clone's `vault/`).
   - Pass → read `git -C <base> status --porcelain` first, then commit only the
     paths this iteration wrote (never `add -A` — the vault may auto-sync
     mid-iteration and sweep someone else's work into your commit):
     `git -C <base> add <paths> && git -C <base> commit -m "chore: loop iteration — <task>"`.
     Any repo code/skill changes commit separately in this repo.
   - Fail → `git -C <base> checkout -- <those same paths>` to discard its writes, then
     re-dispatch once with your feedback appended to the brief. Two fails →
     stop and ask the user.

5. Between iterations, give the user one line: task, verdict, lesson. This
   is the human gate — pause if they want to steer.

6. When all tasks are done (or you're blocked), use the `reflect` skill
   yourself to close out the orchestration session: what the loop achieved,
   what the decomposition got wrong, one lesson.

## Rules

- Never parallelize memory-writing subagents.
- Never commit an unverified iteration. Verification is what separates
  compounding from thrashing.
- If a subagent's reflection contradicts your verification (it says done,
  the check fails), record YOUR verdict in the daily note — external
  verification outranks self-report.
- Improvement passes (the improve skill) stay in the main session,
  interactive. Never delegate improve to a subagent.

Versions

RepositoryScoreLicence
This page · slogsdon/loop-and-gate-foundation100MIT
mickeyyaya/evolve-loop100Apache-2.0
relai-ai/agent-eval-loop100Apache-2.0
SethGammon/Citadel100MIT
alirezarezvani/claude-skills100MIT
x-cmd/x-cmd100Apache-2.0
in-the-loop-labs/pair-review100Apache-2.0
railly/skills100MIT
Mineru98/skills-store100MIT

Database snapshot: 13.11.2026 · package version 3d7b46c7bca3 (SHA-256 digest of the entire package)

Downloads in the last 30 days: 0 · ranking

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