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Intelligent skill router and creator for Claude Code and Codex. Analyzes any input to recommend existing skills, improve
From Art to Engineering: A Manifesto for AI Skill Creation.
The central challenge in AI development isn't a lack of ideas, but the inconsistent process of turning them into robust, reliable skills. Current methods are often ad-hoc, brittle, and difficult to scale—resembling more of an art form than a predictable engineering discipline.
Quality is built in, not bolted on.
SkillForge is a methodology where rigor is integrated into every step of the creation process, from initial conception to final validation. It's a fundamental shift from reactive testing to proactive engineering.
v5.2 adds the Context Skill Advisor: proactive, evidence-backed skill suggestions with user-controlled Proactivity Levels.
off, quiet, balanced, and activebalanced is the default level# Configure proactive advising
python scripts/install_skillforge.py
# Run a checkpoint from the current agent session
python scripts/context_advisor.py checkpoint --cwd "$PWD" --text "<brief current context>"
# Run scheduled advising and queue suggestions
python scripts/context_advisor.py run --cwd "$PWD"
# Review queued suggestions
python scripts/context_advisor.py list
v5.1 builds on the v5.0 context-efficient redesign and adds stronger frontmatter support, hooks guidance, validation coverage, and packaging safety.
The foundation from v5.0 remains: the context window is a public good. Every line in SKILL.md competes with the user's actual work.
references/ where they're loaded only when neededdescription field for pre-load routingSkills now use only name and description in frontmatter. The description field is the primary triggering mechanism — it determines when a skill activates, so all "when to use" information belongs there.
---
name: my-skill
description: What this skill does and when to use it. Include trigger scenarios.
---
A new design concept for matching instruction specificity to task fragility:
New init_skill.py creates rich skill templates with TODO placeholders, organizational pattern suggestions, and example resource files:
python scripts/init_skill.py my-new-skill --path ~/.codex/skills
Iteration is now built into Phase 3. Skills improve through real usage, not just synthesis panel review.
v5.1 expands skill metadata support and documentation:
model, context, agent, hooks, and user-invocablePreToolUse, PostToolUse, and Stopv5.1 adds stronger guardrails for safe distribution:
.skillignore enforcement restored in packagingSkillForge implements its philosophy through a rigorous, autonomous 4-phase architecture. This structure ensures that every skill undergoes comprehensive analysis, thorough specification, clean generation, and objective approval before it is complete.
Before creating anything, SkillForge analyzes your input to determine the best action:
# These all work - SkillForge routes automatically:
SkillForge: create a skill for automated code review
→ Creates new skill (Phase 1-4)
help me debug this TypeError
→ Recommends debugging skills
do I have a skill for Excel?
→ Searches and recommends matching skills
Maximum depth before a single line is generated.
Every problem is systematically deconstructed through 11 distinct thinking lenses, with degrees of freedom assessed for each design decision.
The 11 lenses include: First Principles, Inversion, Second-Order Effects, Pre-Mortem, Systems Thinking, Devil's Advocate, Constraints, Pareto, Root Cause, Comparative, and Opportunity Cost.
Translating deep analysis into a flawless build.
The insights from analysis are codified into a structured XML specification, then used to generate the skill with fresh context. Phase 3 now includes an explicit iteration step — review output against spec, identify gaps, and refine before panel review.
A panel of experts demands unanimous approval.
A generated skill is submitted to a panel of specialized agents, each evaluating against distinct criteria. Approval must be unanimous.
The panel includes:
Skill quality is not enough on day one. The system must stay maintainable and extensible as the skill ecosystem grows.
| Principle | Implementation |
|---|---|
| Engineer for Agents | Standardized directory structure, simplified frontmatter, automated validation |
| Systematize Rigor | 4-phase architecture, regression questioning, 11 thinking lenses, multi-agent synthesis |
| Design for Evolution | Dedicated Evolution agent, mandatory ≥7/10 timelessness score, degrees of freedom assessment |
SkillForge is designed so skills can execute repeatable work, validate outputs, and support autonomous operation where appropriate.
skillforge/
├── SKILL.md # Main skill definition (< 500 lines)
├── LICENSE # MIT License
├── CONTEXT.md # Repo glossary (not packaged)
├── docs/adr/ # Repo architecture decisions (not packaged)
├── commands/ # Claude Code slash commands
│ └── skillforge.md
├── references/ # Loaded into context when needed
│ ├── regression-questions.md
│ ├── multi-lens-framework.md
│ ├── specification-template.md
│ ├── evolution-scoring.md
│ ├── synthesis-protocol.md
│ ├── script-integration-framework.md
│ ├── script-patterns-catalog.md
│ ├── degrees-of-freedom.md
│ └── iteration-guide.md
├── assets/ # Used in output, never loaded into context
│ └── templates/
│ ├── skill-spec-template.xml
│ ├── skill-md-template.md
│ └── script-template.py
└── scripts/ # Automated quality gates and advisor tools
├── advisor_scoring.py
├── context_advisor.py
├── context_sources.py
├── init_skill.py
├── install_skillforge.py
├── install_workshop.sh
├── skillforge_config.py
├── triage_skill_request.py
├── discover_skills.py
├── validate-skill.py
├── quick_validate.py
└── package_skill.py
Key distinction: references/ = loaded into context to inform the model's reasoning. assets/ = used in output, never loaded into context.
For the AI for Ecommerce workshop, run this one-command installer from a regular terminal:
curl -fsSL https://raw.githubusercontent.com/tripleyak/SkillForge/main/scripts/install_workshop.sh | bash
This installs SkillForge for Claude Code, Codex, and the shared local skill surface; installs the Claude Code /skillforge command; configures proactive advising at the default balanced level; and verifies the install.
Restart Claude Code and Codex after installation so the new skill and command load.
# Install (excludes repo-only files like README.md automatically)
git clone https://github.com/tripleyak/SkillForge.git /tmp/skillforge
# Codex install
cp -r /tmp/skillforge ~/.codex/skills/skillforge
rm -rf ~/.codex/skills/skillforge/{README.md,LICENSE,CONTEXT.md,docs,.git,.gitignore,.skillignore} ~/.codex/skills/skillforge/assets/images
# Claude Code install
cp -r /tmp/skillforge ~/.claude/skills/skillforge
rm -rf ~/.claude/skills/skillforge/{README.md,LICENSE,CONTEXT.md,docs,.git,.gitignore,.skillignore} ~/.claude/skills/skillforge/assets/images
# Or package as .skill file (respects .skillignore)
python scripts/package_skill.py /tmp/skillforge ./dist
# Configure proactive advising
python ~/.codex/skills/skillforge/scripts/install_skillforge.py
# Full autonomous execution
SkillForge: {goal}
# Natural language activation
create skill for {purpose}
# Generate specification only
skillforge --plan-only
# Scaffold a new skill
python scripts/init_skill.py my-skill --path ~/.codex/skills
Note:
README.md,LICENSE,CONTEXT.md,docs/adr/, andassets/images/are for GitHub browsing only. They are excluded from.skillpackages via.skillignoreand should not be copied into your skills directory.
SkillForge is a systematic methodology for quality and repeatability.
By codifying expert analysis, rigorous specification, and multi-agent peer review into a fully autonomous system, SkillForge provides a blueprint for building the next generation of robust, reliable, and evolution-aware AI skills.
It transforms skill creation from an art into an engineering discipline.
MIT License — see LICENSE
balanced as install defaultcontext_advisor.py, advisor_scoring.py, context_sources.py, skillforge_config.py, and install_skillforge.py~/.codex/skills) with uppercase SKILL.md supportmodel, context, agent, hooks, user-invocable).skillignore enforcement in packaging workflow.skillignore exclusionsname + description onlydescription for pre-load routinginit_skill.py scaffold script干净、强大、属于你的 AI Agent 平台 --AI agents, without the clutter.
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