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Skill Update

Organization
transilienceai
skill-update

Skill creation, update and management — generates skill directory structure, validates against best practices, enforces line count limits. Use when creating, updating, or improving skills.

Overview

Publishertransilienceai
Repositorycommunitytools
Skill nameskill-update
Stars
565
Forks
81
Bundled files
4
LicenseMIT
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Skill Update 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/transilienceai/communitytools/tree/main/skills/skill-update
  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/transilienceai/communitytools.git /tmp/communitytools
mkdir -p .claude/skills
cp -r /tmp/communitytools/skills/skill-update .claude/skills/skill-update
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Update 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 Update 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 Update 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 Update

Generate or refine Claude Code skills following Anthropic best practices.

Hard caps (enforced by scripts/skill_linter.py)

  • SKILL.md ≤ 150 lines
  • reference/*.md ≤ 200 lines (reference/scenarios/*.md ≤ 400 lines)
  • README.md ≤ 100 lines
  • Every SKILL.md has YAML frontmatter with name + description
  • No DO NOT / MUST NOT / NEVER outside an ## Anti-Patterns section
  • No challenge-specific identifiers (machine names, lab IDs, lab IPs, preserved flags)
  • Every Markdown link resolves to an existing file
  • Every reference file is linked from at least one other file (no orphans)

Principles

  • Brevity first. Every file short, simple, human-readable. Challenge every token.
  • Progressive disclosure. SKILL.md navigates; reference/ holds detail; reference/scenarios/ holds concrete exploit flows.
  • Separation of concern. SKILL.md = WHAT + when. reference/role-*.md = HOW agents behave when spawned.
  • Single canonical home for any cross-cutting rule (output discipline, credential loading, brute-force, etc.). Other files reference, never restate.

File structure

skills/<skill-name>/
├── SKILL.md           # ≤150 lines, YAML + navigation
├── reference/
│   ├── *-principles.md  # ≤150 lines (decision tree)
│   ├── INDEX.md
│   ├── *.md             # patterns, ≤200 lines
│   └── scenarios/
│       └── <category>/
│           └── *.md     # ≤400 lines, self-contained
└── README.md          # optional, ≤100 lines

SKILL.md template

yaml
---
name: <skill-name>
description: What it does AND when to use. Include trigger phrases.
---

# <Skill Name>

<one-paragraph scope>

## When to use

- <bullet>

## Workflow / Quick start

<≤30 lines>

## References

- [reference/...](reference/...)

## Anti-Patterns

- <when negative framing is genuinely needed, put it here>

Run it

The procedure is a workflow — every step is code-enforced, so none can be skipped.

Workflow('skill-update', { output_dir: 'projects/<engagement>' })   // harvest an engagement
Workflow('skill-update', { learnings: [{ text, technique_type }] }) // judge a known set
Workflow('skill-update', { mode: 'audit' })                         // read-only, writes nothing

Add dryRun: true to see the write plan without writing. Parent-orchestrator only.

Phases. Intake (baseline skill_linter.py --json) → Harvest (reframe learnings) → Judge (four gates + blind refuters) → Route (author the block) → Write (persist verbatim) → Sweep (confidentiality guard) → Verify (linter delta, not an absolute clean tree — the base carries pre-existing violations).

Every promote/reject/write decision is pure JS in .claude/workflows/lib/wf-helpers.mjs (promotionGate, capBudget, writeGate, lintDelta, skillUpdateGate). No agent decides whether a learning is promoted or a write is allowed.

The four-gate promotion test

Process the techniques and failure modes from completed engagements. Promote a learning to the skill base only if all four hold:

  1. Generalizable. Reusable pattern, not target-specific lore. No machine names, lab IDs, target IPs, preserved flags, writeup attributions.
  2. Material improvement. Adds coverage, efficiency, or decision-quality for future engagements.
  3. Not already captured elsewhere in the skill base. (scripts/skill_linter.py flags duplicates.)
  4. Minimal footprint. Prefer extending an existing entry over adding a new file. Keep the base lean and high-signal.

Reframing recipe

Always frame as a reusable pattern: "when encountering X condition, try Y approach" — never "on box-N, Y worked". Use <TARGET_IP>, <DC_FQDN>, <DOMAIN> placeholders in tool examples.

Output

Three buckets, built in code so a run cannot claim an edit it did not make: Updated. / Skipped. (with the gate that failed) / No changes.

Reference

Anti-Patterns

  • Creating CHANGELOG.md / SUMMARY.md / VERIFICATION.md auxiliary files.
  • Meta-documentation about the creation process inside the skill itself.
  • Verbose inline templates and examples (link to reference/ instead).
  • Re-introducing duplicate rule prose (brute-force, output-dir, env-reader).
  • Files past their cap — split into reference/ immediately.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Skill Update AI skill do?

Skill creation, update and management — generates skill directory structure, validates against best practices, enforces line count limits. Use when creating, updating, or improving skills.

Why use Skill Update on TypingMind?

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

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

Which AI models can use Skill Update?

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 Update?

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

Is the Skill Update 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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