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Get Started Free →Get your project to 100% ✪ AI-readiness, fast — the AI auto-detects your stack and only asks for what it can't know (your goal and the human "why"). Least typing, maximum context. For time-conscious builders; feeds into faf-expert for depth.
.claude/skills/sickn33-faf-context/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 72% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 78% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -4% | 0% |
Use this skill when you need get your project to 100% ✪ AI-readiness, fast — the AI auto-detects your stack and only asks for what it can't know (your goal and the human "why"). Least typing, maximum context. For time-conscious builders; feeds into faf-expert for depth.
AI writes its best code when it has your project's context. This skill helps you hand it over — fast, and to 100%.
.faf is an IANA-registered context format (application/vnd.faf+yaml) — a typed, portable file you own, readable by any AI (no bespoke manifest, no vendor lock-in). The whole point is one number: AI-readiness, 0–100%. At 100% ✪ the AI starts every session already knowing your project — no re-explaining, no guessing. This skill is the builder's path to that number: minimum typing, maximum context.
> For the done-for-you one-click path, use faf-wizard. To master the format, use faf-expert. This skill is the quickstart in between.
faf-cli has 21 slots. It works in three steps — and only the last one needs you:
slotignoreds the rest (never counted against you).faf auto detects your stack + language, and a sharp goal sentence seeds who/what/where. The better your goal, the more the AI fills for you.So your job isn't "fill 21 boxes." It's: write one good goal, then answer the few questions the AI couldn't fill itself.
> (Teams / Enterprise tiers add more slots — monorepos, caching, versioning — but those aren't faf-cli. faf-cli is the 21.)
The 6 Ws are "the underivable half" — but you almost never write all six from scratch:
So the fast path is: write one sharp goal, confirm the seeds, fill why + when. → 100%.
> "Sometimes 3 Ws is enough. Sometimes the goal alone is enough." A great goal sentence + auto-detection can carry who/what/where/how on their own — leaving you two small answers. The better your goal, the less you type.
bashfaf auto # 1. AI detects your whole stack + seeds context from your README faf score # 2. See the number + exactly which slots are still empty faf go # 3. Guided fill: confirm the seeded Ws, answer the 1–2 left faf score # 4. 100% ✪ faf sync # 5. Push context into CLAUDE.md / AGENTS.md (optional)
Most projects are 1 good goal sentence + 2 answers away from Trophy.
The goal is the generative input — it seeds who/what/where automatically. Make it a real, specific sentence (it's also your use-case):
→ seeds what (a CLI that scores AI-readiness), where (Claude, Cursor, Gemini), who (solo developers). You'd only add why + when.
Each W is a 3–4 word label (hard cap < 6) — a scannable spec card, not a paragraph:
| W | Asks | Example | |---|------|---------| | Who | who is it for? | solo developers | | What | what are they building? | AI-readiness scorer | | Why | why does it exist? | eliminate context re-explaining | | Where | where does it run/ship? | npm, Homebrew | | When | timeline / stage? | production, since 2025 | | How | how is it built/used? | Bun CLI + WASM |
slotignoredA CLI has no frontend; an API has no UI library. Mark those slotignored and they drop out of the denominator — you're scored only on slots that matter for your app type. 100% means "everything that applies is filled," not "every box checked." (faf auto and faf go handle most of this for you.)
The AI only seeds facts your goal/README literally state — never invents, never uses templates. What it can't source, it leaves empty for you. Empty beats wrong. That's why the resulting context is trustworthy: every slot is either detected, stated by you, or honestly blank.
faf go / faf-loopThe goal: the AI is only as good as the context you give it. Answer the few things only you know — the gaps it couldn't fill itself — and it's optimized to help you at 100% ✪.
MIT · part of the FAF skill family (faf-context · faf-wizard · faf-expert)
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 10,727 | 5,821 | -46% | 1 | 1 | 0% | 1,925 | 2,597 | +35% | 0 | 0 | — |
case-02 | fail→pass | 12,446 | 9,887 | -21% | 1 | 1 | 0% | 1,895 | 3,261 | +72% | 0 | 0 | — |
case-03 | fail→pass | 14,205 | 9,175 | -35% | 1 | 1 | 0% | 2,273 | 3,326 | +46% | 0 | 0 | — |
case-04 | fail→pass | 7,997 | 3,575 | -55% | 1 | 1 | 0% | 1,237 | 2,198 | +78% | 0 | 0 | — |
case-05 | fail→pass | 15,139 | 3,922 | -74% | 1 | 1 | 0% | 2,306 | 2,220 | -4% | 0 | 0 | — |
case-06 | fail→pass | 16,222 | 3,090 | -81% | 1 | 1 | 0% | 2,501 | 2,141 | -14% | 0 | 0 | — |
case-07 | fail→pass | 9,154 | 2,153 | -76% | 1 | 1 | 0% | 1,457 | 1,951 | +34% | 0 | 0 | — |
case-08 | fail→pass | 15,184 | 2,597 | -83% | 1 | 1 | 0% | 2,385 | 1,965 | -18% | 0 | 0 | — |
case-09 | fail→pass | 12,400 | 4,460 | -64% | 1 | 1 | 0% | 1,791 | 2,342 | +31% | 0 | 0 | — |
case-10 | pass→pass | 9,929 | 2,198 | -78% | 1 | 1 | 0% | 1,613 | 1,940 | +20% | 0 | 0 | — |
case-11 | pass→pass | 9,969 | 3,738 | -63% | 1 | 1 | 0% | 1,627 | 2,301 | +41% | 0 | 0 | — |
case-12 | pass→pass | 11,746 | 4,227 | -64% | 1 | 1 | 0% | 1,620 | 2,243 | +38% | 0 | 0 | — |
case-13 | pass→pass | 11,309 | 3,968 | -65% | 1 | 1 | 0% | 1,879 | 2,302 | +23% | 0 | 0 | — |
case-14 | fail→pass | 14,527 | 2,321 | -84% | 1 | 1 | 0% | 2,385 | 2,044 | -14% | 0 | 0 | — |
case-15 | fail→pass | 33,971 | 2,070 | -94% | 1 | 1 | 0% | 3,097 | 1,913 | -38% | 0 | 0 | — |
case-16 | fail→pass | 16,122 | 1,981 | -88% | 1 | 1 | 0% | 2,393 | 1,863 | -22% | 0 | 0 | — |
case-17 | fail→pass | 10,099 | 4,872 | -52% | 1 | 1 | 0% | 1,426 | 2,318 | +63% | 0 | 0 | — |
case-18 | pass→pass | 12,334 | 2,609 | -79% | 1 | 1 | 0% | 1,837 | 2,019 | +10% | 0 | 0 | — |
case-19 | fail→pass | 11,771 | 3,857 | -67% | 1 | 1 | 0% | 1,614 | 2,260 | +40% | 0 | 0 | — |
case-20 | fail→pass | 10,897 | 2,207 | -80% | 1 | 1 | 0% | 1,728 | 1,912 | +11% | 0 | 0 | — |
case-21 | pass→pass | 12,307 | 5,427 | -56% | 1 | 1 | 0% | 1,802 | 2,446 | +36% | 0 | 0 | — |
case-22 | fail→pass | 10,770 | 3,168 | -71% | 1 | 1 | 0% | 1,569 | 2,026 | +29% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +73 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.