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Squid Clean Harness

Community
iusztinpaul
squid-clean-harness

Shrink an agent harness (.agents/ skills + resources) to the minimum tokens that keep the exact same logic. Never touches resources/datasets/.

Overview

Publisheriusztinpaul
Repositorysquid
Skill namesquid-clean-harness
Stars
207
Forks
34
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by iusztinpaul on GitHub. Read the source before you install it.

Installation

Install the Squid Clean Harness AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/iusztinpaul/squid/tree/main/skills/squid-clean-harness
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/iusztinpaul/squid.git /tmp/squid
mkdir -p .claude/skills
cp -r /tmp/squid/skills/squid-clean-harness .claude/skills/squid-clean-harness
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Squid Clean Harness in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Squid Clean Harness on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Squid Clean Harness is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Clean harness — same logic, minimum tokens

You are the cleaner. You shrink the skills and resources a harness loads; you do not change what they make the agent do. Zero logic diff is the contract — every capability, step, and rule that holds before must hold after; only tokens die.

$ARGUMENTS is an optional .agents/ path (e.g. ~/Vaults/Second-Brain/.agents). Empty means the repo's ./.agents.

When NOT to use — out of scope, no exceptions

ExcludedWhy
resources/datasets/Data the skills operate on, not logic. Never in scope.
Memory files (AGENTS.md, CLAUDE.md)→ /squid-clean-memory.
Docs, READMEs, comments, docstrings→ /squid-clean-docs.
Skill names and argument shapesThe name is the API — a rename is an edit, not a cleanup.

Step 1 — Resolve the target set

Inventory skills/*/SKILL.md and resources/** minus resources/datasets/ under the target .agents/. Everything must be revertible: clean in git, or snapshot first (cp -R .agents .agents.bak) when the tree isn't tracked.

Step 2 — Audit the contract (frontmatter)

Two questions per skill:

  • Invocation type — auto-fired by the model, or only typed by the user? User-only → disable-model-invocation: true, keeping its description out of the always-loaded skill list. Flipping also cuts reach from other skills — grep the set for the skill's name first (Step 4's proof); one referrer means it stays model-invoked.
  • Description — must match what the body does. Model-invoked → triggers only, one per branch: synonyms naming the same branch are duplication ("build with TDD" / "asks for test-first development" is one branch written twice). User-only → one sentence of what it does; trigger prose serves no router — delete it.

Done only when every skill in the set has both answers recorded — no sampling.

Step 3 — Apply the keep/delete test

An instruction survives only if deleting it would change what the agent does. The cuts apply to every file in the set — skill bodies and resources/ docs alike. Five cuts:

Duplicates — the same rule twice in one skill, repeated across skills, or restating a resources/ doc the skill already points at (a skill body repeating glossary.md definitions) — the resource is the home, the skill keeps the pointer. Two resources repeating each other collapse to one home the same way.

Baked-in claims — would the agent already behave this way with the line deleted (harness default, system-prompt rule)? Dead weight. "Read the file before editing it" dies; "write posts in the voice defined in resources/branding/" stays. The test is harness-relative — .agents/ serves non-Claude agents too: delete only defaults universal to whatever loads the skill; flag the Claude Code-specific ones. Test sentence by sentence; a failing sentence dies whole — never trim words from it.

Verbose logic — the exact same logic in the minimum words: collapse restatements, prefer a table row to a paragraph, caveman-terse wording. Collapse a restated quality into one pretrained leading word ("fast, deterministic, low-overhead" → a tight loop). If the caveman plugin is installed, /caveman-compress per file is a good first draft — re-check afterwards that the frontmatter is still parseable YAML.

Merges — steps or sections nobody reads apart become one; reorder so each idea appears once, at the point the agent needs it.

Disclosure — a skill body is paid for on every invocation, including the branches that never read it. Inline what every branch needs; push what only some branches reach — a lookup table, a per-platform appendix, a rare edge-case procedure — into a resources/ doc loaded on demand. Same home for a block ≥2 skills share. Size decides: ~20+ lines earns its own file, below that indirection costs a Read for less than it saves. The pointer must say when to load it, not just where it lives ("for the Slack variants, read platforms.md", not "see platforms.md"). Theory: /squid-write-skill.

Done only when every file in the set is read whole and judged against all five cuts.

Step 4 — Dangling artifacts

A reference, script, agent, or resources/ doc is dead only with zero references outside its own definition:

sh
grep -rn "helper.py" <harness-root> <project-root>   # prove it, don't assume
  • A rule dangles by staleness, not references — nobody cites a rule, they read it. It dies when what it governs no longer exists: a step for a removed tool, a caveat for a branch that's gone.
  • Referenced but missing → a broken contract, not a deletion: flag it and ask.
  • Present but unreferenced → delete only on clean proof; a helper called from outside .agents/ (cron, vault automation) is invisible to grep — when unsure, flag, never auto-delete.

Done only when every artifact is proven dead, alive, or flagged.

Step 5 — Plan of attack (the output artifact)

Print one table in chat. Do not write it to disk unless the user asks.

FileCut (Step 2–4)Why~Words
skills/search/SKILL.mdfrontmatteruser-only; trigger prose serves no router−130
skills/post/ ∩ skills/thread/duplicateshared hook rules → one home in resources/−200
skills/article/SKILL.mdverboseStep 2 restated three ways−90
skills/publish/SKILL.mddisclosureper-platform appendix; 1 of 4 branches−240
resources/glossary.mdverboseeach term defined twice, prose + table−150

Stop and wait for explicit approval. Do not edit before it.

Step 6 — Execute

Apply the approved plan, one commit per category (frontmatter, cuts, dangling) so any revert is surgical.

Step 7 — Verify

Logic must be provably unchanged before hand-off:

  • Every SKILL.md frontmatter still parses as YAML with name + description — unparseable YAML loads the skill with every field silently dropped.
  • Every remaining reference (file, skill, script, agent) resolves, and every disclosed file has a pointer that states when to load it.
  • Per-file logic diff: every step and rule in the old version is still stated or merged — name its new home.
  • No skill or argument was renamed.

Anything off: revert that commit (or restore the snapshot), do not "fix forward".

Step 8 — Hand-off

Report wc -w before → after per file, what moved into resources/, and every flagged dangling item. Point the user at /squid-write-skill for flagged items that need rewriting rather than cutting — an edit is theirs to make, and only they can invoke it.

Frequently asked questions

What does the Squid Clean Harness AI skill do?

Shrink an agent harness (.agents/ skills + resources) to the minimum tokens that keep the exact same logic. Never touches resources/datasets/.

Why use Squid Clean Harness on TypingMind?

Because you install it once and use it with any model. Squid Clean Harness is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Squid Clean Harness in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/iusztinpaul/squid/tree/main/skills/squid-clean-harness. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Squid Clean Harness?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Squid Clean Harness?

As many as you like. As long as a model supports skills, you can use Squid Clean Harness with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Squid Clean Harness AI skill free?

Yes. It is published on GitHub by iusztinpaul under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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