| name | skillforge |
| description | Use when creating, improving, finding, or auditing agent skills - the user says 'create a skill', 'do I have a skill for X', 'improve the X skill', 'which skill should I use', asks whether a skill exists for a task, or wants to validate, test, evaluate, package, or health-check skills. Also use for skill ecosystem maintenance (duplicate detection, stale skills, trigger collisions) and advisor checkpoints. |
| license | MIT |
| user-invocable | true |
| allowed-tools | ["Read","Glob","Grep","Bash","Write","Edit","Task"] |
| metadata | {"version":"6.0.0","domains":["meta-skill","skill-creation","skill-testing","orchestration","routing"],"type":"orchestrator"} |
SkillForge 6 - Skill Router, Creator & Ecosystem Maintainer
Routes any skill-related request to the right action (use, improve, create, compose), creates new skills through an evidence-driven pipeline, and maintains the health of the whole skill ecosystem. Core principle: skill quality is a property of behavior, not documents - a skill is done when a fresh agent demonstrably does better with it than without it.
Routing (Phase 0)
Always triage before creating anything:
python3 scripts/discover_skills.py
python3 scripts/triage_skill_request.py "<the user's request>" --json
| Triage result | Action |
|---|
| Strong match (existing skill) | Recommend it; do not create a duplicate |
| Moderate match | Offer IMPROVE_EXISTING on the matched skill |
| Weak/no match + create intent | Proceed to creation pipeline |
| Multi-domain | Suggest composing existing skills |
| Ambiguous | Ask one clarifying question |
Match bands are keyword-evidence heuristics, not calibrated probabilities - report them as "strong/moderate/weak match", never as percent confidence.
Creation pipeline
Run phases in order. Each phase's detailed procedure lives in its reference - read the reference when you reach the phase, not before.
0. Baseline gate (RED). Before designing anything, dispatch a fresh subagent (Task tool) on 1-2 representative target tasks WITHOUT the skill. Capture verbatim what it does wrong. If the baseline does not fail, stop - the skill is unnecessary. The failures become the skill's test cases and its description keywords. See references/testing-and-evals.md.
1. Analysis. Identify explicit, implicit, and discovered requirements. Apply the three load-bearing lenses - Inversion (what guarantees failure → anti-patterns), Pareto (which 20% of scope delivers 80% → cut the rest), Root Cause (is this the real problem?) - plus any others from references/multi-lens-framework.md that earn their tokens. Classify the failure type you are guarding against and match the guidance form to it (see the failure-form table in references/testing-and-evals.md). Choose instruction specificity with references/degrees-of-freedom.md. Decide scripts with references/script-integration-framework.md.
2. Specification. Write the spec using references/specification-template.md. Minimal tier (problem, requirements, decisions with WHY, success criteria, test scenarios) for most skills; full tier (temporal projection, obsolescence triggers, extension points) only for infrastructure skills. Never fill a section you cannot ground - omit it.
3. Generation in fresh context. Dispatch a subagent (Task tool) that receives ONLY the spec and the baseline failures - not the analysis transcript - to write SKILL.md and supporting files. Scaffold first: python3 scripts/init_skill.py <name> --path <skills-dir>. Description doctrine: trigger conditions only, third person, symptom keywords, never a workflow summary. Budget: SKILL.md under 1,500 words; move depth to references/; <details> tags save zero tokens for agents - do not use them.
4. Execution testing (GREEN). Re-run the baseline tasks WITH the skill via fresh subagents. Gate on behavioral delta: the with-skill runs must not exhibit the baseline failures. Then run the description-triggering check (positive and near-miss queries). Iterate description and body against observed failures, not hunches. For improvements to existing skills, use blind A/B judging. Full protocols: references/testing-and-evals.md.
5. Review = lint + one adversarial reviewer. Mechanical gates first:
python3 scripts/validate_skill.py <skill-dir>
python3 scripts/check_docs_safety.py <skill-dir>
Then one fresh-context subagent prompted to REFUTE the skill (find the case where it misleads, over-triggers, or fails its own scenarios), carrying the reviewer checklists in references/synthesis-protocol.md. Fix what it proves; ship what survives. Do not convene approval panels - same-model unanimity measures nothing.
6. Ship with evals. Every generated skill keeps its tests: an evals/ directory (trigger queries + behavioral scenarios + assertions) so future edits can be regression-tested with python3 scripts/run_skill_evals.py <skill-dir>. Iterate post-ship with references/iteration-guide.md.
Frontmatter and platform facts
Write frontmatter against the current Claude Code field set (17 fields) documented in references/claude-code-frontmatter.md, which also covers hooks (hooks receive JSON on stdin, not env vars), context: fork/agent, $ARGUMENTS, and the agentskills.io portability limits (64-char name, 1024-char description) that validate_skill.py enforces. Never pin dated model IDs (claude-*-YYYYMMDD) - the validator rejects them.
Ecosystem maintenance
python3 scripts/skillforge_doctor.py
python3 scripts/compile_skill.py <dir> --target claude|codex|agentskills
python3 scripts/package_skill.py <dir> ./dist
python3 scripts/mine_skill_friction.py --consent
Use doctor output to drive IMPROVE_EXISTING work; use friction reports as advisor evidence.
Context Skill Advisor
Proactive suggestions are delivered through Claude Code hooks (SessionStart surfaces the queue; UserPromptSubmit scores checkpoints inline) - no daemon. Configure with python3 scripts/install_skillforge.py (interactive; hooks and Personal Context scanning are opt-in, never default). Manage the queue: python3 scripts/context_advisor.py list|use|snooze|dismiss. Suggestions are evidence-backed and never auto-invoke a skill.
Script inventory
| Script | Purpose |
|---|
discover_skills.py | Build/refresh the cross-runtime skill index |
triage_skill_request.py | Route input to use/improve/create/compose/clarify |
validate_skill.py | Full structural + lint validation (quick_validate.py = fast subset) |
run_skill_evals.py | Run a skill's evals/ regression suite |
skillforge_doctor.py | Ecosystem health report |
init_skill.py | Scaffold a new skill (with evals/) |
compile_skill.py | Compile a skill for a target runtime |
package_skill.py | Package as .skill archive |
mine_skill_friction.py | Opt-in transcript friction mining |
context_advisor.py / install_skillforge.py | Advisor queue and setup |
check_docs_safety.py | Unsafe interpolation check |
Script exit codes: 0 success, 1 failure, 2 usage/consent error, 10 validation failure, 11 verification/dependency failure.
Extension points: new lint checks in validate_skill.py; new doctor checks in skillforge_doctor.py; new compile targets in compile_skill.py; new lenses in references/multi-lens-framework.md.
Anti-patterns
| Avoid | Instead |
|---|
| Creating without a failing baseline | Run the RED gate; no failure = no skill |
| Description that summarizes workflow | Trigger conditions only - agents act on summaries and skip the body |
| Body "Triggers" sections as a mechanism | Only the frontmatter description drives invocation |
| Approval panels and self-scored gates | Lint what is falsifiable; adversarially refute the rest |
<details> blocks for "progressive disclosure" | Separate reference files loaded on demand |
| Pinned dated model IDs | Family aliases or omit model: |
| Duplicating an existing skill | Phase 0 triage first, always |
Verification checklist
