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Skill Generation

Organization
aspenkit
skill-generation

LLM-powered generation pipeline for Claude Code skills and AGENTS.md — doc-init command, prompt system, context building, and output parsing

Overview

Publisheraspenkit
Repositoryaspens
Skill nameskill-generation
Stars
102
Forks
7
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Skill Generation 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/aspenkit/aspens/tree/main/.agents/skills/skill-generation
  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/aspenkit/aspens.git /tmp/aspens
mkdir -p .claude/skills
cp -r /tmp/aspens/.agents/skills/skill-generation .claude/skills/skill-generation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Generation 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 Generation 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 Generation 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.

You are working on aspens' skill generation pipeline — the system that scans repos and uses Claude/Codex CLI to generate skills, hooks, and instructions files.

Domain purpose

aspens doc init orchestrates a multi-step LLM pipeline that turns a scanned repo + import graph into a base skill, per-domain skills, and an instructions file (AGENTS.md or AGENTS.md). Generation is always done in Claude-canonical format and transformed for other targets afterwards. The end product is what other coding agents (and aspens' own hooks) consume to stay grounded in the repo.

Critical files (purpose, not inventory)

  • src/commands/doc-init.js — the pipeline orchestrator (backend → target → scan → graph → discovery → strategy → mode → generate → validate → transform → write → hooks → recommended extras → config)
  • src/lib/runner.js — runLLM(), loadPrompt(), parseFileOutput(), validateSkillFiles() shared across all LLM-driven commands
  • src/lib/skill-writer.js — writes parsed files, generates skill-rules.json, injects domain bash patterns, merges settings.json
  • src/lib/skill-reader.js — parses skill frontmatter, activation patterns, keywords (consumed by skill-writer)
  • src/lib/git-hook.js — installGitHook() / removeGitHook() for post-commit auto-sync (monorepo-aware)
  • src/lib/timeout.js — resolveTimeout() for auto-scaled + user-override timeouts
  • src/lib/target.js / src/lib/backend.js / src/lib/target-transform.js — target/backend resolution and Claude→other-target transform
  • src/prompts/ — doc-init.md, doc-init-domain.md, doc-init-claudemd.md, discover-domains.md, discover-architecture.md, plus partials/ (skill-format, preservation-contract, examples)

Key Concepts

  • Pipeline steps: (1) detect backends (2) backend selection (3) target selection (4) scan + graph (5) existing docs discovery check (6) parallel discovery agents (7) strategy (8) mode (9) generate (10) validate (11) transform for non-Claude targets (12) show files + dry-run (13) write (14) install hooks (Claude-only) (15) recommended extras (save-tokens, agents, git hook) (16) persist config to .aspens.json
  • Early config persistence: Target/backend config is written to .aspens.json before generation starts (after step 4), so a failed generation run still records the user's explicit target/backend choice. saveTokens from existing config is preserved. Final writeConfig at step 16 adds saveTokens from recommended install.
  • --recommended flag: Skips interactive prompts with smart defaults. Reuses existing target config from .aspens.json. Auto-selects backend from target. Defaults strategy to improve when existing docs found. Auto-picks discovery skip when docs exist. Auto-selects generation mode based on repo size. Also installs save-tokens, bundled Claude agents, dev/ gitignore entry, and doc-sync git hook (step 15).
  • Recommended extras (step 15): When --recommended and not --dry-run: calls installSaveTokensRecommended() from save-tokens.js (if Claude target), copies all bundled agent templates to .claude/agents/ (skips existing) via installRecommendedClaudeAgents(), adds dev/ to .gitignore, installs doc-sync git hook if not present. Summary lines printed after.
  • Backend before target: Backend selection (step 2) happens before target selection (step 3). If both CLIs available, user picks backend first, then targets. Pre-selects matching target in the multiselect. With --recommended, backend is inferred from existing target config.
  • Canonical generation: All prompts receive CANONICAL_VARS (hardcoded Claude paths: .claude/skills, skill.md, AGENTS.md, .claude). Generation always produces Claude-canonical format regardless of target. Non-Claude targets are produced by post-generation transform via transformForTarget().
  • Incremental writing (chunked mode): When mode === 'chunked' and not dry-run, generated files are written to disk as each chunk completes instead of waiting until the end. User is prompted to confirm incremental writes before generation starts. Helper functions: validateGeneratedChunk() validates and strips truncated files per chunk; buildOutputFilesForTargets() handles multi-target transform; writeIncrementalOutputs() deduplicates and writes changed files. Tracks written content via incrementalWriteState (contentsByPath + resultsByPath Maps). When incremental mode is active, post-generation validation/transform/confirm/write steps are skipped (already done per-chunk).
  • parseLLMOutput with strict single-file fallback: Codex often returns plain markdown without <file> tags. parseLLMOutput(text, allowedPaths, expectedPath) only wraps tagless text as the expected file for true single-file prompts (exactly one exactFile in allowedPaths, no dirPrefixes). Multi-file prompts require proper <file> tags.
  • Existing docs reuse: When existing Claude docs are found and strategy is improve, loadExistingDocsContext() inlines them as ## Existing Docs (improve these — preserve hand-written rules...) into the prompt. chooseReuseSourceTarget() decides whether Claude or Codex docs are the source. Supports cross-target reuse (e.g. Claude docs → Codex output).
  • Domain reuse helpers: loadReusableDomains() tries loadReusableDomainsFromRules() first (reads skill-rules.json), falls back to findSkillFiles() with extractKeyFilePatterns() parsing ## Key Files blocks.
  • Config persistence with target merging: Uses mergeConfiguredTargets() to avoid dropping previously configured targets. writeConfig now also persists saveTokens config from the recommended install.
  • Hook installation: Only for targets with supportsHooks: true (Claude). installHooks() generates skill-rules.json, copies hook scripts, injects generated domain patterns into post-tool-use-tracker.sh via # BEGIN/END detect_skill_domain markers, merges settings.json (backs up existing to .bak).
  • Git hook offer: With --recommended, git hook is auto-installed (no prompt). Without --recommended, interactive prompt offered. Detection looks for the marker string aspens doc-sync hook (<rel>) in .git/hooks/post-commit.
  • Discovery agents: Two LLM calls run in parallel — discover-domains (hub files + domain clusters) and discover-architecture (hub files + ranked + hotspots). Findings are merged into discoveryFindings and parsed; domain-specific slices are injected into each domain prompt as ## Discovery Findings for {domain}.

Critical Rules

  • Base skill + instructions file are essential — pipeline retries up to 2× with format-correction prompts when <file> tags are missing. Domain skill failures are acceptable (user retries with --domains).
  • improve strategy preserves hand-written content — LLM must read existing skills first and not discard human-authored rules. The preservation-contract partial enforces this in every prompt.
  • Discovery runs before user prompt — domain picker shows discovered domains, not scanner directory names. Falls back to scanner domains if discovery fails.
  • PARALLEL_LIMIT = 3 — domain skills generate in batches of 3 concurrent calls. Base skill always sequential first. Instructions file always sequential last.
  • CliError, not process.exit() — all error exits throw CliError; cancellations return early.
  • --hooks-only is Claude-only — hardcoded to TARGETS.claude regardless of config.
  • Incremental write deduplication — writeIncrementalOutputs() skips files whose content hasn't changed since last write, using contentsByPath Map for tracking. Directory-scoped AGENTS.md files (path ends with /AGENTS.md but not the root AGENTS.md) go through writeTransformedFiles(), all others through writeSkillFiles().
  • Read-only LLM tools — generation calls always pass allowedTools: ['Read', 'Glob', 'Grep']. The LLM explores the repo itself; aspens never lets it write.
  • AGENTS.md post-processing — generated instructions files are run through ensureRootKeyFilesSection(), syncSkillsSection(), and syncBehaviorSection() so aspens owns the Skills list and Behavior block deterministically; prompts explicitly forbid the LLM from emitting these sections.

References

  • Prompts: src/prompts/doc-init*.md, src/prompts/discover-*.md
  • Partials: src/prompts/partials/skill-format.md, src/prompts/partials/preservation-contract.md, src/prompts/partials/examples.md

Last Updated: 2026-05-11

Frequently asked questions

What does the Skill Generation AI skill do?

LLM-powered generation pipeline for Claude Code skills and AGENTS.md — doc-init command, prompt system, context building, and output parsing

Why use Skill Generation on TypingMind?

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

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

Which AI models can use Skill Generation?

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 Generation?

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

Is the Skill Generation AI skill free?

Yes. It is published on GitHub by aspenkit 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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