Skill Prune

Organization

transilienceai

skill-prune

Identify and remove negative-ROI skill content — orphan files, never-read entries, duplicates, content reintroducing challenge-specific lore. Inverse of /skill-update. Use during quarterly maintenance or after the linter flags issues.

Overview

Publisher	transilienceai
Repository	`communitytools`
Skill name	`skill-prune`
Stars	565
Forks	81
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 transilienceai on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/transilienceai/communitytools/tree/main/skills/skill-prune
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.

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/transilienceai/communitytools.git /tmp/communitytools
mkdir -p .claude/skills
cp -r /tmp/communitytools/skills/skill-prune .claude/skills/skill-prune

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Prune 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 Skill Prune 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 Skill Prune 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.

Skill Prune

Inverse of /skill-update. Removes content rather than adding it. Run during quarterly maintenance, after engagements, or when scripts/skill_linter.py reports orphans / duplicates.

When to invoke

Quarterly cadence.
After scripts/skill_linter.py --check-orphans reports orphan reference files.
After a SKILL.md or reference file grows past its cap and needs trimming.
After a de-specialization sweep, to drop content tied to retired challenges.

Prune criteria (the four signals — same shape as /skill-update, inverted)

A reference / scenario / line is a prune candidate when it satisfies any of:

Orphan — not linked from any SKILL.md or other reference file in the last 60 days.
Referenced only by failed engagements — appeared in attack-chain.md of runs that ended status=BLOCKED, never in a successful chain.
Contradicted by newer content — a later scenario / pattern supersedes it; the older entry no longer reflects current technique.
Redundant with newer content — same technique covered more clearly elsewhere.

Removing content fails any of these → keep it.

Safety rules

Never prune a file with  annotation.
Never prune content cited in a still-open engagement's OUTPUT_DIR/attack-chain.md.
Never prune the canonical-home file for a single-owner rule (brute-force, output-discipline, env-reader, skill-update).
Bias toward keeping technique-rich content over operational lore.

Procedure

Run scripts/skill_linter.py --check-orphans to surface orphans.
For each candidate file or block, evaluate the four signals.
Build a deletion plan — show files / lines to remove with one-line rationale per item.
Apply deletions only after the plan is approved (skill-prune does not auto-delete during invocation).
Re-run scripts/skill_linter.py to confirm the change broke no other links and didn't reintroduce duplicates.

Output

Concise change report:

Removed. File or block + one-line rationale per item.
Kept (flagged for follow-up). Items that failed all four signals but are worth revisiting next quarter.
No prunes. State explicitly when nothing warranted removal.

Anti-Patterns

Pruning content because it's old — age alone is not a signal; relevance is.
Pruning a reference because its parent SKILL.md is bloated — fix the SKILL.md instead.
Removing a single-owner canonical file (brute-force / output-discipline / env-reader).
Pruning during an active engagement that may still cite the content.

Frequently asked questions

What does the Skill Prune AI skill do?

Why use Skill Prune on TypingMind?

Because you install it once and use it with any model. Skill Prune 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 Skill Prune in TypingMind?

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

Which AI models can use Skill Prune?

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 Skill Prune?

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

Is the Skill Prune AI skill free?

Yes. It is published on GitHub by transilienceai under the MIT 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Skill Prune

When to invoke

Prune criteria (the four signals — same shape as /skill-update, inverted)

Safety rules

Procedure

Output

Anti-Patterns

Frequently asked questions

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