skill-foundry

name	skill-foundry
description	Use when creating, optimizing, reviewing, or extracting reusable Agent Skills, SKILL.md, AGENTS.md, prompts, or agent instructions; or when designing instruction architecture and evals. Triggers on 写/改/审查 skill, skill 质量, 提示词工程, 指令设计, create/optimize/review skill, prompt engineering, instruction design. Not for application code, multi-agent runtime architecture, general prose, or explanation/summary/translation requests that do not create or change instructions.
license	MIT
compatibility	Bundled tooling requires Python >=3.10 and Bash >=3.2 on macOS or Linux. WSL is expected but not CI-verified.

SkillFoundry

A skill is not a prompt. It is a design system for AI behavior — architecture, vocabulary, constraints, and craft working together to make domain-specific behavior more repeatable. Whether it improves real outcomes must be demonstrated against a baseline; the document itself is never proof.

The difference between a mediocre skill and an excellent one is not more rules. It is sharper language, tighter structure, and the discipline to make every sentence do exactly one job. A generation skill (writing, design, image) controls output quality. A methodology skill (debugging, reviewing, researching) changes how AI thinks. They share structure but differ in every design decision — interaction model, mode system, failure handling, and language voice.

Core Principle

Precision over flexibility. The more specific your vocabulary, the more consistent AI's output.

• "Use a nice background" → AI picks its training-data default (gradient, blur, or flat color).
• "Paper base, subtle grain, ink wash or contour field, and optional WebGL canvas" → AI produces a layered editorial atmosphere.

The same principle applies everywhere: layout descriptions, constraint wording, quality checks, failure examples. Vague instructions produce average output. Precise vocabulary produces distinctive output.

Outcome Contract

• Outcome: A skill with tight structure, precise vocabulary, reliable triggers, and verifiable quality.
• Done when: A Scaffold is ready for labeled trial use after Routing + Contract pass; it may not claim proven effectiveness. Production requires all 4 layers (Routing / Contract / Content / Regression). Content uses persisted comparative evidence or a stronger controlled business metric; no layer passes by self-attestation.
• Evidence: Public Agent Skills specifications, bounded research on long-context position and persuasion effects, runnable structural checks, and task-specific baseline comparisons. Public sources and claim limits are listed in the repository README.
• Output: SKILL.md + references/ + optional scripts/, or concrete optimization recommendations for an existing skill.

Capability Circle

✅ Write new skills from scratch or conversation patterns ✅ Optimize existing skills across 7 dimensions ✅ Review and score skill quality with evidence ✅ Extract reusable patterns from conversations ❌ Not for multi-agent runtime architecture — if the problem is agent orchestration, state management, or execution engines, skill instructions will not fix it ❌ Not for model capability gaps — if the model cannot reason about X, no SKILL.md will teach it ❌ Not for business logic code — SkillFoundry designs AI behavior, not application features

Mode Router

Signal	Mode
"写一个 skill" / "create a skill" / starting from scratch	Write — full 7-step workflow
"优化这个 skill" / "效果不好" / "improve"	Optimize — diagnose, locate, fix
"看看写得怎么样" / "review" / "audit"	Review — 7-dimension scoring
"把对话提取成 skill" / "extract"	Extract — identify patterns, distill, test

Methodology vs. Generation: if the target skill changes how AI thinks (debugging, reviewing, researching), read references/craft-and-voice.md § Methodology Skill Design before starting. Its interaction model, mode system, and language voice are fundamentally different from a generation skill.

Working With the User

SkillFoundry's users span from skill veterans to someone who heard the word "skill" today. Meet them where they are — the workflow serves the skill, not the reverse.

• Enter at the user's step, don't restart. If they arrive with a draft description, start at Step 2; if they have a finished skill that mis-fires, go straight to Optimize. Re-running the full 7 steps on someone who is 80% done is friction, not rigor.
• Match their vocabulary. Use "eval", "frontmatter", "CSO", "holdout" only after they signal they know the terms; otherwise name the thing in one plain line before using the jargon. A confused user can't give you good feedback.
• One artifact beats three questions. When inputs are thin, produce a draft skill (or a draft description) with assumptions marked, and let them react to something concrete — per the 3-question budget in references/behavior-control.md.
• Honor "just vibe with me." If the user wants to skip the process and think out loud, do that. Pull in the rigor (eval, run-compare, holdout) when there's something worth hardening, not as a gate on every exchange.

Thought Interceptors

"This skill looks straightforward" means run the 7-dimension scoring — do not skip to writing. "The description is fine as-is" means test it with 3+3 routing prompts in a fresh session. "I already know how to structure this" means read references/architecture.md — follow the 4 decisions, not your training default. "Adding more rules will make it better" means check if existing rules conflict first and pressure-test the combined set. "This is a generation skill" means verify — does it change output format, or does it change how AI thinks? Misclassification produces the wrong design patterns.

Write Mode

Two tracks through the same steps. Scaffold (default for a first version): Steps 0-2 in full, then a minimal body — frontmatter, opening statement, workflow, a few hard rules, output format — and a 3+3 routing fixture with one positive and one negative holdout; ship it as unproven trial use and let real usage generate Gotchas. Production (for distribution or after a Scaffold survives real use): add Content evidence and regression gates. Mode Router, Direction Lock, Capability Circle, and Gotchas remain conditional; empty scaffolding is context tax.

0. Qualify (gate)

Before anything else, decide whether this should be a skill at all — the highest-leverage SkillFoundry decision is often "don't build one." Build only if at least one holds:

• the workflow will be reused (not a one-off answer),
• it is easy to mis-route — a near-neighbor request the agent handles wrong by default,
• a deterministic script removes repeated manual work, or
• the output needs a reusable contract that others depend on.

Reject — do the task inline, or hand back a near-neighbor — when the request is only: explain, summarize, translate, brainstorm, document-without-agent-execution, or a one-off with no reuse. These are exactly the near-misses that make a meta-skill wrongly fire; they belong in the routing fixture as negatives (evals/trigger_cases.example.jsonl), not as new skills.

Even when it should be captured, a skill is not the only vehicle — route to the right one (a skill fires non-deterministically, so the wrong vehicle silently fails):

The need	Vehicle	Why not a skill
A durable rule/fact that must hold every session	AGENTS.md / CLAUDE.md	Always in context; a must-always rule put in a skill loads only sometimes
A deterministic action that must run every time an event fires	a hook	Bypasses the trigger gamble entirely
A big, context-heavy sub-task to isolate	a subagent	Keeps main context clean; a skill runs inline and competes for attention
An occasionally-needed reusable workflow/contract	a skill	The one case skills are genuinely best at

The most common mis-vehicle is putting a must-always rule into a skill: skills under-trigger, so the rule you needed every time shows up only some of the time.

1. Discover

Before writing anything, answer five questions:

1. What mistake does AI make without this skill? Name it: "AI 在做 XX 时默认用 YY，但应该用 ZZ." If you cannot name the specific default behavior and the specific replacement, you do not have a problem statement yet. "帮助用户做 XX" is a wish, not a problem.
2. What does AI do by default? Run the task without any skill. Save the output — this is your baseline. If the baseline is already good, you do not need this skill.
3. How will users trigger it? Write 5-8 natural phrases — the exact words a person would type, in the language they would type them. These become your description keywords.
4. Where does the expertise come from? Point at real source material — a runbook, past corrections, expert answers, failure records, house style docs. A skill grounded only in the model's general knowledge produces precisely worded genericness: the instructions read sharp, but the content is what the model would have said anyway. No source material → the run-compare delta in Step 6 will be thin, and you'll discover it late. Gather the material now or lower the ambition.
5. Draft the eval before the body. Turn the trigger phrases from 1.3 and the baseline failures from 1.2 into concrete eval cases (should-trigger / should-not-trigger / expected output) now — before writing any instructions. Anthropic's official guidance: "Create evaluations BEFORE writing extensive documentation." Evals written after the body test what you wrote; evals written before test what users need.

2. Name It (Description)

The description is the skill's front door. Write it wrong and the skill is invisible.

Read references/description-and-triggering.md for CSO rules. The essentials:

• What + when: one clause naming the capability, then "Use when..." trigger scenarios. Never workflow steps (the "how").
• Include the exact keywords from Step 1.3 in the languages users actually use, with precise and fuzzy phrasing. Skills under-trigger more often than they over-trigger, so lean slightly generous and rein in with "Not for X".
• ≤ 1024 characters. Sweet spot: 200-400.

Then lint it before moving on: python3 scripts/trigger_eval.py lint --skill <skill-dir>. Fix every FAIL (empty / over-length / workflow-leak) before writing the body — a broken routing surface makes the rest invisible.

3. Architect

Read references/architecture.md. Four decisions:

1. Can SKILL.md stay at or below 500 lines? If not → justify what must remain resident and split conditional material; >700 fails the default validator.
2. Which content is only needed in specific scenarios? → Conditional reference: "when X, read Y."
3. Are there rules that can be verified by script? → Put them in scripts/, execute don't read. Run scripts/validate-skill.sh <skill-dir> to check metadata and structural compliance signals; use task evals to test whether Hard Rules are actually followed.
4. Is this the first usable version? → Start from templates/scaffold-starter.md. Use templates/skill-starter.md only for Production complexity. A template with marked editable regions beats ten pages of formatting rules.

4. Write the SKILL.md

Use modules by track; empty completeness is not the goal. Scaffold requires Frontmatter, Opening Statement, Workflow, Hard Rules, Output Format, and a 3+3 routing fixture. Production adds the modules whose conditions below are true:

Frontmatter          route (decides if skill activates)
Opening Statement    philosophy (1-2 sentences — set the mental model)
Outcome Contract     add for externally checked deliverables
Capability Circle    add for real scope or refusal boundaries
Mode Router          add when 2+ distinct execution modes exist
Direction Lock       add before expensive/irreversible work
Workflow             the creative process (each step names its reference)
Hard Rules           absolute boundaries (MUST / NEVER / No exceptions)
Anti-Rationalization add after an observed shortcut
Reference Map        add when references/ exists
Gotchas              add after real failure stories exist
Output Format        deliverable spec

The canonical track and threshold rules live in references/invariants.md; explanations elsewhere must not redefine them.

Read references/prompt-patterns.md for the detailed craft of each section — including Direction Lock, Decision Tree, Premise Breakdown, and Reasoning Model considerations.

Read references/craft-and-voice.md for the language dimension — vocabulary precision, style identity tests, voice as design decision, the 6-layer instruction staircase, gate failure path design, calibration rules, one-shot question design, and information density as rhythm.

The Opening Statement sets the tone for everything. It is the first thing AI reads after activation. A sharp opening — "If it could have been generated by a default prompt, it is not good enough" — reframes AI's entire approach. A bland opening — "This skill helps with X" — changes nothing.

5. Write the References

Each reference file mirrors a professional concept:

File role	Real-world analogy	Example file
Style system	Brand guidelines	style-system.md
Pattern library	Component catalog	layout-recipes.md
Design tokens	Variable definitions	components.md, theme-presets.md
Quality gate	QA checklist	qa-checklist.md
Domain knowledge	Subject-matter reference	terminology-zh.md

Rules:

• One depth from SKILL.md. Every reference must work standalone: optional peer links are allowed, required peer loads are not.
• Descriptive filenames (api-patterns.md, not doc2.md).
• TOC at top if > 100 lines.
• Route co-required files directly from SKILL.md instead of building a nested load chain.

6. Test (4-Layer Eval)

1. Routing (runnable, not self-attested): write a fixture of ≥3 should-trigger + ≥3 should-not-trigger prompts as JSONL (see evals/trigger_cases.example.jsonl). Mark ≥1 positive + ≥1 negative as "holdout": true — you tune the description against the rest, then prove it on these unseen cases (anti-overfit; a description tuned to pass cases it has already seen proves nothing).
   • Tune: trigger_eval.py worksheet --skill <dir> --fixture <f> --phase train → judge each shuffled, unlabeled prompt in a fresh context (description only, no body), write {"id":N,"decision":"trigger"|"no"} per line, score, and fix the description until train passes.
   • Validate: worksheet ... --phase holdout, judge in a fresh context, score. A single-phase run gates that phase; a combined run requires both train and holdout to pass. Misses come back tagged UNDER-TRIGGER (widen keywords) or OVER-TRIGGER (tighten / add "Not for").
2. Contract (runnable): express deterministic requirements in a contract JSON and run python3 scripts/contract_eval.py --contract <contract.json> --output <artifact>. Put subjective requirements in manual_checks and supply criterion-level --manual-results; pending manual checks exit 2 and never count as pass.
3. Content (comparative, not self-attested): use a controlled business metric when one exists; otherwise run baseline vs with-skill. Persist the task, raw outputs, exact blind candidates, judge inputs, runs.json, judges.json, mapping, and verdict; create and verify the manifest with scripts/evidence.py. Exploratory evidence may use one run per side but cannot support a stable-effect claim. Production requires ≥2 matched run pairs; every pair gets both A/B judge positions. Record model IDs and token/time metrics when available; when the host does not expose them, record an explicit unavailable reason instead of inventing values. Full protocol and v2 schema: references/quality-assurance.md § Run-Compare and references/evidence-schema.md.
4. Regression: After changes, do previously passing cases still pass? Keep the fixture; re-run score (and a run-compare on the changed path) after every meaningful change.

Before shipping, run python3 scripts/citation_lint.py --skill <dir> — the body must not name private skills a reader cannot open. After changing SkillFoundry itself, run bash scripts/regress.sh --library-root <loaded-skills-dir>; it executes checker bite tests plus persisted routing/evidence regressions and the library audit.

If you are adding this skill into a library that already has others, also run python3 scripts/skill_library_audit.py (pass every --root the host actually loads — user skills, project skills, plugin caches; it scans recursively). Routing collision is a property of the set, not the skill: the platform has no algorithmic router — the model reads every description and picks one, so a skill that routes perfectly alone can steal or lose triggers once it sits next to 20+ siblings. The audit flags description-budget pressure (the real ceiling is host-specific and dynamic — hosts drop or truncate descriptions past their budget; the script's default is a heuristic, tune with --budget), unroutable skills (empty / over-1024 description), duplicate names, and overlapping trigger vocabulary. Step 1 routing (trigger_eval) tests one skill; this tests the library.

For skills with multiple supported models or hosts, test every declared supported combination when feasible; otherwise test at least one stronger and one weaker representative and label the remaining matrix unverified. A single-model skill does not add artificial model breadth.

Read references/quality-assurance.md for the full eval framework, domain slop catalog methodology, and Design Invariants. Read references/iteration.md for TDD for Skills (RED → GREEN → REFACTOR) and pressure testing.

7. Iterate

Every test failure is a signal:

• A new real-world failure case → append to Gotchas, do NOT lengthen the main workflow. The workflow stays the stable spine; failures accumulate in the table. A workflow that grows with every incident becomes unreadable and unfollowable.
• AI took a shortcut → add an Anti-Rationalization entry (read references/behavior-control.md)
• AI ignored a rule → check its position. Long-context middle placement can degrade retrieval depending on task and model; move critical rules to the head/tail and rerun the failing case.
• AI output is inconsistent → reduce freedom. Provide a template, a decision tree, or tighter constraints.
• AI followed the rule but the output is wrong → the rule itself is broken. Fix the rule, not the AI.

Capture loop: after the agent uses the skill, ask where it went off-track and what context was missing, then turn verified failures into a Gotchas row or fixture. Iterate on one hard task until it succeeds, then generalize.

Read the transcript, not just the output. The output says whether the result is right; the transcript says how the skill shaped the process. Watch for three signals — the skill sent the agent on a detour (cut that instruction), the agent re-wrote the same helper across runs (extract it to scripts/), or with-skill burned far more tokens than baseline for no quality gain (trim the body). Script extraction should come from observing repeated work, not from armchair guessing — guessed scripts become dead code nobody calls. See references/iteration.md § 读 transcript.

Optimize Mode

Read the target skill, then diagnose across 7 dimensions (priority order):

1. Trigger accuracy — Does the description match real user language?
2. Information architecture — Is the skill at or below 500 lines by default? Are references independently usable?
3. Instruction precision — Are constraints bright-line rules, or vague suggestions? Do any constraints conflict with each other?
4. Output reliability — Is there a validation script? A seed template?
5. Behavior control — Can AI find a rationalization to skip a step?
6. Iteration support — Are Gotchas from real failures? Is there version tracking?
7. Token efficiency — Is anything loaded that is not used?

For each: current state → problem → specific fix.

Review Mode

Score 1-5 on each dimension. Cite specific lines as evidence:

Dimension	1	5
Trigger precision	Vague description, no keywords	CSO-optimized, tested with 3+3
Architecture	Single file >500 lines	Three-layer progressive disclosure
Instruction clarity	"consider doing X"	Bright-line rules + NO/OK contrast
Behavior control	No safeguards	Anti-Rationalization + Hard Stops
Output reliability	AI judgment only	Script validation + seed template
Iteration support	No Gotchas	Real failure stories + version log
Token efficiency	Everything inline	Conditional loading + script execution

< 20 → rewrite. 20-28 → targeted optimization. 29-35 → structurally mature and ready for Production eval; the score never proves effectiveness.

Extract Mode

Turn a successful conversation into a reusable skill:

1. Identify instructions and constraints that recurred across turns.
2. Separate reusable patterns from project-specific details — extract only the former.
3. Write a minimal SKILL.md covering only the identified patterns.
4. Test in a fresh session — can AI reproduce the conversation's behavior from the skill alone?

Handoff

When SkillFoundry cannot help — the problem is at the architecture layer, model capability layer, or outside the instruction design domain:

Problem: [what the user is trying to solve]
Tried: [which SkillFoundry approaches were attempted]
Why insufficient: [architecture / model capability / out of scope]
Suggest: [alternative approach, tool, or discipline]

Do not keep iterating on SKILL.md when the root cause is not in instructions. A structured handoff is more valuable than a sixth rewrite.

Hard Rules

• SKILL.md body should stay ≤ 500 lines. At 501–700, explain why the content must remain resident or split low-frequency material into references; >700 fails the default validator. Do not pad, and do not amputate necessary instruction just to hit a number.
• Description MUST NOT contain workflow steps. Only trigger conditions.
• References MUST be discoverable one depth from SKILL.md and independently usable. Optional peer links are allowed; required reference-to-reference loads are not.
• Match constraint register to stakes (three tiers, never "consider/try to"): (1) non-negotiable / irreversible / safety → MUST / NEVER / No exceptions; (2) everything else → imperative + the reason ("Do X, because Y") — explaining why outperforms a bare MUST on smart models, and stacking MUSTs on ordinary rules dilutes the ones that matter (official skill-creator calls all-caps ALWAYS/NEVER on routine rules a "yellow flag"); (3) never weak words like "consider", "try to", "you might want to". See references/prompt-patterns.md § Authority 用词.
• Do not explain concepts AI already knows. Do not teach what Markdown is.
• Every sentence in a skill is a tax — indexed descriptions cost every session, loaded bodies cost every turn. For each sentence ask: without it, would the agent err? If not, delete it.
• One excellent example beats five mediocre ones. Never duplicate examples across languages just for coverage.
• Every constraint should be specific enough to visualize. "Do not use decorative blobs" is testable. "Keep it clean" is not.
• The body (SKILL.md + references) MUST NOT name private/internal skills a reader cannot open — a name with no verifiable content is noise that reads like an ad. Keep the technique and any inlined evidence; drop the name. Public, checkable sources (Anthropic spec, agentskills.io, Meincke PNAS 2026, Lost in the Middle) are fine. Enforce with scripts/citation_lint.py; public provenance belongs in CHANGELOG.md or an evidence note.

Reference Map

File	What it provides	Load when
templates/scaffold-starter.md	Minimal first-version template: opening, workflow, hard rules, output, routing-fixture reminder	Default in Write Mode
templates/skill-starter.md	Production module template with conditions and deletion guidance	After Scaffold survives usage or distribution requires more controls
references/invariants.md	Single source of truth for metadata, size, module tracks, reference, eval, evidence, and authority rules	Before changing any repeated threshold or checker
references/evidence-schema.md	Complete v2 format for matched runs, bound contract reports, blind candidates, and per-pair position swaps	Before creating Production content evidence
scripts/validate-skill.sh	Automated checks: line count, description, standalone references, map consistency, authority wording, TOC	Step 6 — run bash scripts/validate-skill.sh <skill-dir>
scripts/contract_eval.py	Executes deterministic Output Contract checks and blocks ungraded manual criteria	Step 6 Contract layer
scripts/evidence.py	Creates and verifies v2 run-compare manifests, repeated-run metadata, blind candidate identity, and position swaps	Step 6 after raw artifacts are saved, and before citing a verdict
scripts/citation_lint.py	Scans the body for private/internal skill names a reader cannot open; fails if any leak in	Step 6 and before publishing
scripts/self-check.sh	Runs the checker suite on good inputs and bite tests each guard with broken inputs	After editing any script
scripts/regress.sh	One command for self-check, structure, routing evidence, run-compare integrity, and optional library audit	Before publishing SkillFoundry changes
scripts/trigger_eval.py	Runnable Routing eval: lint (description defects), worksheet --phase train/holdout (fresh-context judging), score (complete phase coverage; combined runs gate both train and holdout; maps under/over-trigger to fixes)

Gotchas

What happened	Rule
Description contained "dispatches subagent per task with code review between tasks." AI ran only 1 review cycle, not 2.	Description is a trigger, not a summary. Putting workflow in the description causes AI to skip the body — it thinks it already knows what to do.
SKILL.md was long; middle-positioned Hard Rules were missed in repeated task runs.	Long-context retrieval can be weaker in the middle. Keep critical rules near the head/tail, split low-frequency material, and verify on the failing task.
Wrote "始终检查工作目录" as a rule. AI still operated in the wrong directory.	Abstract rules are invisible. Concrete failures are memorable: "Moved files to ~/project, but the repo was at ~/www/project — lost 2 hours." Gotchas work through pattern matching, not principle comprehension.
Used "consider doing X" for a safety check. AI skipped it under time pressure from the user.	"Consider" grants permission to skip; "MUST" does not — weak words are semantically optional. (Indirect support: LLMs are measurably sensitive to authority framing — Meincke et al. PNAS 2026 — but the binding reason is semantic, and your own run-compare is the direct test.)
Wrote instructions in generic language: "use a good layout." AI repeatedly fell back to generic defaults in the tested cases.	Increase only the vocabulary precision needed to remove that observed ambiguity, then compare again.
AI said "I'm following the spirit, not the letter" and skipped TDD.	Add one sentence: "Violating the letter IS violating the spirit." This blocks the entire class of rationalization.
Reference files required each other in a loop. AI bounced between them, wasting tokens and losing context.	Each reference must be independently usable. Optional peer links are fine; required peer-load chains are not.
A gate said "check the photo has a quiet zone" but didn't say what to do when it fails. AI added a dark overlay — the exact hack the skill was designed to prevent.	Every gate needs a designed failure path: degrade, switch container, change input, honest exit — and an explicit ban on the tempting workaround.
Added aggressive anti-slop rules. AI corrected so thoroughly it changed the author's voice and meaning.	Anti-Rationalization prevents doing too little; Calibration Rules prevent doing too much. Every behavior control system needs both sides.
Wrote a methodology skill (debugging) using generation-skill patterns: linear workflow, heavy seed templates, auto-validation. AI followed the steps but still skipped diagnosis and jumped to patching.

Anti-Patterns (Quick Self-Check)

The full list with NO/OK contrast is in references/anti-patterns.md (don't restate it here). Top scan:

• Description contains workflow steps, or the body exceeds ~500 lines without justification.
• Constraints use "consider" / "try to" / "you might want to".
• Narrative content or generic labels (helper1, step3) instead of named, specific instruction.
• Deployed without testing, or @file.md force-loads a large file every session.
• Language is generic enough that removing the skill name would make the instructions unrecognizable.