skill-generation

A generation pipeline that scans a code repository and uses an AI coding tool to create skills and instruction files for agents. These files describe the project so agents can work with its code more accurately.

In plain words
What is it for?
Use it to create or update repository skills, generate agent instructions, build context for different code areas, and validate and write the resulting files.
Why use it?
It removes the need to write all project-specific agent guidance by hand and bases the guidance on the repository's files and dependencies.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/aspenkit/aspens/skill-generation
Any agent
npx skills add aspenkit/aspens --skill skill-generation
Clone the repo
git clone --depth 1 https://github.com/aspenkit/aspens

Made for: Claude Code, Codex.

Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,074 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00030 $0.02074
Opus 5 $0.00015 $0.01037
Sonnet 5 $0.00006 $0.00415
Haiku 4.5 $0.00003 $0.00207

Measured yesterday against content hash db09c275d245, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

skill-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/skill-generation/SKILL.md · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.jsrunLLM(), 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.jsinstallGitHook() / removeGitHook() for post-commit auto-sync (monorepo-aware)
  • src/lib/timeout.jsresolveTimeout() 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}.

Read the full file on GitHub · 65 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 65 lines · 30 tokens per session scan A db09c275d245

Subscribe to this mod's changes

skill-generation is a skill published in the GitHub repository aspenkit/aspens (96 stars, last pushed 16d ago), licensed MIT. It adds 30 tokens to every session and 2,074 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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