The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Skill creation harness for Claude Code and Codex
SkillForge is a staged pipeline for building skills, checking them against a baseline, and shipping them with evals. It discovers existing skills, routes requests, generates specs, compiles skills across runtimes, and runs regression checks plus ecosystem health audits.
Builders who make or maintain skills for Claude Code and Codex and want behavior-based validation.
You can ship skills with tests and health checks instead of relying on the document alone.
What it does
Baseline and green gates
Runs a task without the skill first, then reruns it with the skill and checks for a behavioral improvement.
Per-skill regression evals
Ships each skill with `evals/` and a runner so the same trigger cases can be checked later.
Ecosystem doctor
Finds duplicate triggers, stale references, budget issues, and pinned models across your skill set.
Cross-runtime compile
Compiles one skill for `claude`, `codex`, or `agentskills` from the same source.
Opt-in advisor and friction mining
Uses hooks and consented local history to suggest skills and surface gaps without default personal scanning.
Skill scaffolding and packaging
Creates new skill folders, validates them, and packages them into `.skill` artifacts.
How to get it
- 1Run
git clone https://github.com/tripleyak/SkillForge.git /tmp/skillforge cp -r /tmp/skillforge ~/.claude/skills/skillforge cd ~/.claude/skills/skillforge && rm -rf README.md LICENSE CONTEXT.md docs .git .gitignore .skillignore index.html assets/images scripts/tests SKILLFORGE_AUDIT.md cp /tmp/skillforge/commands/skillforge.md ~/.claude/commands/skillforge.md # optional /skillforge command
- 2Optional advisor + hooks (interactive, everything opt-in)
python3 ~/.claude/skills/skillforge/scripts/install_skillforge.py
README
SkillForge v6
A skill creator that proves its skills work.
SkillForge routes any skill-related request (use, improve, create, compose), creates new skills through an evidence-driven pipeline, and maintains the health of your whole skill ecosystem. Its 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.
What v6 changed (and why)
v6 is a ground-up rework following a deep external audit (see SKILLFORGE_AUDIT.md on the repo, not shipped with the skill). The headline shifts:
| v5 | v6 |
|---|---|
| 4-agent "unanimous synthesis panel" reads the skill | Skills are executed: baseline (RED) runs before writing, with-skill (GREEN) runs after, behavioral delta is the gate |
| Self-scored "timelessness >= 7" approval | Falsifiable checks moved to lint (validate_skill.py); one adversarial reviewer refutes what lint can't catch |
5,049-word SKILL.md with <details> "progressive disclosure" | 1,158-word SKILL.md; depth lives in references/ loaded on demand |
| Descriptions = "what this skill does" | Descriptions = trigger conditions only (workflow summaries make agents skip the body) |
launchd background advisor that analyzed / and queued into a file nothing read | Advisor delivered through Claude Code hooks (SessionStart + UserPromptSubmit), caps enforced, opt-in |
| Personal-directory scanning on by default, hardcoded GitHub handles | Personal Context strictly opt-in with recorded consent; no shipped defaults |
| Hand-rolled YAML parser that failed SkillForge's own SKILL.md | One shared typed parser (scripts/frontmatter.py), 100+ unit tests, and a regression test that SkillForge validates itself |
| Index missed the Claude Code plugin cache entirely | Cross-runtime discovery: personal, Codex, Claude Code plugin cache; deduped; auto-refresh |
The pipeline
Phase 0 TRIAGE index + word-boundary matching -> USE | IMPROVE | CREATE | COMPOSE | CLARIFY
Phase 0b RED GATE fresh subagent attempts the task WITHOUT the skill; no failure = no skill
Phase 1 ANALYSIS load-bearing lenses (Inversion, Pareto, Root Cause), failure-form matching
Phase 2 SPEC tiered (minimal default / full for infrastructure), decisions + WHY
Phase 3 GENERATE fresh-context subagent receives ONLY the spec + baseline failures
Phase 4 GREEN GATE with-skill runs must clear the recorded baseline failures; trigger tests
Phase 5 REVIEW lint (validate_skill.py) + ONE adversarial reviewer charged to refute
Phase 6 SHIP with evals/ - a per-skill regression suite runnable forever after
What no other skill creator has
- Dedup before create (Phase 0): an index of every skill across runtimes answers "should this exist?" first.
- Skills ship with their tests:
evals/(trigger queries + behavioral scenarios) +run_skill_evals.py= regression testing for skills. - Ecosystem doctor:
skillforge_doctor.pyfinds trigger collisions between skills, duplicates, stale file references, budget violations, and pinned models across your entire roster. - Cross-runtime compile: author once,
compile_skill.py --target claude|codex|agentskills. - Friction mining (opt-in):
mine_skill_friction.py --consentfinds skill gaps in your own local session history. - Proactive advisor (opt-in): evidence-backed skill suggestions delivered via hooks, never auto-invoked.
Install (Claude Code)
git clone https://github.com/tripleyak/SkillForge.git /tmp/skillforge
cp -r /tmp/skillforge ~/.claude/skills/skillforge
cd ~/.claude/skills/skillforge && rm -rf README.md LICENSE CONTEXT.md docs .git .gitignore .skillignore index.html assets/images scripts/tests SKILLFORGE_AUDIT.md
cp /tmp/skillforge/commands/skillforge.md ~/.claude/commands/skillforge.md # optional /skillforge command
Optional advisor + hooks (interactive, everything opt-in):
python3 ~/.claude/skills/skillforge/scripts/install_skillforge.py
Requirements: Claude Code (or Codex CLI), Python 3.8+ (stdlib only; PyYAML used if present).
Toolbox
| Command | Purpose |
|---|---|
python3 scripts/discover_skills.py | Build/refresh the skill index |
python3 scripts/triage_skill_request.py "<request>" --json | Route a request |
python3 scripts/validate_skill.py <dir> | Full validation + lint |
python3 scripts/run_skill_evals.py <dir> [--live] | Run a skill's regression evals |
python3 scripts/skillforge_doctor.py | Ecosystem health report |
python3 scripts/init_skill.py <name> --path <dir> | Scaffold (includes evals/) |
python3 scripts/compile_skill.py <dir> --target <t> | Cross-runtime compile |
python3 scripts/package_skill.py <dir> ./dist | Package as .skill |
python3 scripts/mine_skill_friction.py --consent | Mine local transcripts for skill gaps |
CI: copy assets/templates/github-workflow-skill-ci.yml into .github/workflows/ of any skill repo.
Releases
Version history and detailed release notes live on the releases page. v6.0.0 is a ground-up rework; the audit that drove it ships in-repo as SKILLFORGE_AUDIT.md.
License
MIT - see LICENSE
Files in the repo
- assets
- commands
- docs
- references
- scripts
- .gitignore
- .skillignore
- CONTEXT.md
- index.html
- LICENSE
- README.md
- SKILL.md
- SKILLFORGE_AUDIT.md
Discussion (0)
Ask about usage, or say what you built with itSign in to join the discussion.
No comments yet. Be the first to say what this is good for.
More harnesses
from vibe coding to agentic engineering - practice makes claude perfect
🌊 The original agent meta-harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
Practical patterns, starters & CLI tools for loop engineering with AI coding agents. Design systems that prompt and orchestrate agents (inspired by Addy Osmani and Boris Cherny). Includes loop-audit, loop-init, loop-cost.
Git. Ship. Done - Core

The most RAM efficient harness