Why Skill Forge?
Skill Forge is the only AI-skills toolchain where every claim your agent reads points back to a real upstream location — a file:line at a pinned commit when source is available, or a documentation URL when it isn’t. Not “sourced from training data.” Not “retrieved from context.” Cited. You can open the upstream repo (or the doc page) and see the function exists — in under a minute. That’s the wedge. This page explains why it matters, how SKF compares to alternatives, and who it’s for.
The problem you’re hiring SKF to solve
Section titled “The problem you’re hiring SKF to solve”Your AI agents read your codebase through the lens of whatever happened to be in their training data. When that training data is wrong, stale, or incomplete, your agent invents — function names that don’t exist, parameter types that don’t match, config options removed two versions ago. You catch some of it in review. You ship some of it by accident. Every sprint, your team spends hours untangling code that only compiles in the AI’s imagination.
SKF treats this as a citation problem, not a model problem. If a skill claims cognee.search() takes query_text as its first parameter, SKF points to cognee/api/v1/search/search.py:L26 at commit 3c048aa4 in the upstream repo. That’s the whole pitch: nothing is made up, and everything is falsifiable in 60 seconds.
How SKF compares
Section titled “How SKF compares”| Approach | What it does well | Where it falls short |
|---|---|---|
Skill scaffolding (npx skills init) | Generates a spec-compliant skill file | The file is empty — you still have to write every instruction by hand |
| LLM summarization | Understands context and intent | Generates plausible-sounding content that may not match the actual API |
| RAG / context stuffing | Retrieves relevant code snippets | Returns fragments without synthesis — no coherent skill output |
| Manual authoring | High initial accuracy | Drifts as the source code changes, doesn’t scale across dependencies |
| IDE built-in context (Copilot, Cursor) | Convenient, zero setup | Uses generic training data, not your project’s specific integration patterns |
| Skill Forge | Every instruction cites upstream — file:line@SHA for source skills, doc URL for docs-only skills. Falsifiable in 60 seconds. | Coverage depends on which tools you’ve installed (Quick / Forge / Forge+ / Deep tiers). |
What “falsifiable in 60 seconds” actually means
Section titled “What “falsifiable in 60 seconds” actually means”Pick any symbol in any SKF-compiled skill. Three clicks:
- Open the skill’s
metadata.json— it names the upstream repo and the exact commit SHA. - Open the skill’s
provenance-map.json— find your symbol; it lists the file and line. - Visit the upstream repo at that commit and that line. The signature in the skill should match.
For docs-only skills, the audit shape is the same — provenance-map.json still lists every symbol — but entries cite [EXT:{url}] instead of file:line@SHA, and step 3 becomes “open the doc URL and confirm the signature matches.”
If it doesn’t, that’s a bug. Open an issue and SKF republishes the skill with a new commit SHA and a new provenance map. No other AI-skills tool treats disagreement between claim and source as a defect. SKF does.
See the Verifying a Skill page for the full three-step audit on real skills, the test reports that log exactly where coverage falls short, and the scoring formula behind the 80% pass threshold.
Who’s this for?
Section titled “Who’s this for?”The curious developer
Section titled “The curious developer”Your agent just hallucinated a method that doesn’t exist, again. You want this to stop, and you don’t want to read a long architecture reference before running your first command. → Start with Getting Started.
The BMAD user
Section titled “The BMAD user”You already use BMAD Method, BMM phases, TEA, or BMB, and you want to know where SKF fits. → Read BMAD Synergy for the phase-by-phase integration playbook.
The skeptic
Section titled “The skeptic”“AI docs for AI” sounds like the problem pretending to be the solution. You want receipts before you install anything. → Start with Verifying a Skill — the three-step audit on real skills, including the 1% that fails.
The OSS maintainer
Section titled “The OSS maintainer”You want to ship verified skills alongside your library releases — npx skills publish-ready, drift-detectable, version-pinned.
→ See Examples → OSS Maintainer Publishing Official Skills.
The team lead evaluating adoption
Section titled “The team lead evaluating adoption”You’re considering running SKF across a brownfield platform. You need to know about rollback safety, [MANUAL] section preservation, and the health-check feedback loop before committing.
→ Start with Architecture, then Workflows → Workflow Health Check.
Not for you if…
Section titled “Not for you if…”- You want docs that hand-hold through every happy path with screenshots and emojis. SKF is a citation machine, not a tutorial series.
- You don’t have Node.js ≥ 22 and Python ≥ 3.10 installed. SKF is a Node/Python toolchain at its core.
- You have neither source code nor published documentation for the target. SKF compiles from one or both — a source repo (citations as
file:line@SHA) or doc URLs (citations as[EXT:{url}]). A vague description with no upstream artifact to cite isn’t enough.
Everything else is downstream of one question: are the instructions your AI reads provably true? If yes, SKF isn’t adding value. If you can’t be sure, SKF is the tool.
- Install SKF — Node ≥ 22, Python ≥ 3.10,
uv, onenpxcommand - Audit a skill in 60 seconds — see the receipts before you install
- Browse real skills — four Deep-tier skills, all shipping their audit trails