Agents that learn by doing — and remember how they did it.
Skills-Driven Development (SkDD) is a methodology where AI agents create, evolve, and share reusable skills as a natural byproduct of their work. Instead of front-loading all knowledge into prompts, agents forge skills on the fly and persist them for future reuse.
A skill is a self-contained directory with a SKILL.md file that follows the Agent Skills open specification. But SkDD goes further: it treats skills as living artifacts — discovered, forked, evolved, and composed by agents across projects and sessions.
Most agent workflows today are stateless: the agent reads a prompt, does work, and forgets. SkDD adds a feedback loop:
Work → Notice a reusable pattern → Forge a skill → Persist it → Discover it next time
This turns agent experience into compound knowledge. The more an agent works, the better it gets — not because the model improves, but because the skill library grows.
| Path | What it is |
|---|---|
docs/ |
The methodology: skill colony concept, forging mechanics, specification alignment |
skillforge/ |
The meta-skill: agents use this to create new skills |
examples/ |
A sample project showing SkDD in action |
colony/ |
The skill colony pattern: discovery, evolution, sharing |
Copy skillforge/SKILL.md into your project's skills directory:
mkdir -p .skills/skillforge
cp skillforge/SKILL.md .skills/skillforge/SKILL.mdOr reference it in your agent configuration:
# AGENTS.md / .claude/settings.json / codex setup
skills:
- skillforge/When an agent encounters a repeatable pattern during development, it can invoke the skillforge to create a new skill:
"I notice I keep scaffolding API endpoints the same way.
Let me forge a skill for this."
The agent creates:
.skills/api-endpoint/
├── SKILL.md # Instructions + triggers
├── scripts/scaffold.sh # Executable template
└── references/
└── conventions.md # Project-specific patterns
Next time any agent works on the project, it discovers the api-endpoint skill automatically. No re-learning. No re-prompting. The pattern is encoded.
When skills accumulate across projects and agents, they form a skill colony — a shared, evolving library of capabilities that agents can discover, fork, and adapt.
See colony/README.md for the full concept.
Project A forges: deploy-preview
Project B forks: deploy-preview → deploy-preview-vercel
Project C discovers: deploy-preview-vercel (via registry)
Agent X evolves: deploy-preview-vercel (adds rollback)
Skills aren't static documentation. They're living process memory.
SkDD is fully compatible with the Agent Skills specification:
| Agent Skills Spec | SkDD Extension |
|---|---|
SKILL.md with YAML frontmatter |
✅ Same format |
scripts/, references/, assets/ |
✅ Same structure |
| Manual skill creation | ➕ Agents forge skills autonomously |
| Static skill libraries | ➕ Skills evolve through use |
| Per-project skills | ➕ Colony-level discovery + sharing |
SkDD doesn't replace the spec — it adds a lifecycle on top of it.
Don't try to anticipate every skill upfront. Let agents create skills when they notice patterns during real work.
Each skill should do one thing well. Complex workflows emerge from composing small skills, not from monolithic instruction sets.
A skill that was forged 3 months ago and never updated is dead weight. Agents should evolve skills when they encounter edge cases or better approaches.
Individual skills are useful. A colony of skills that agents can discover and compose is transformative. Invest in the registry and discovery mechanisms.
Skills are markdown files that humans can read, review, and edit. But they're structured so agents can parse, discover, and execute them without human intervention.
- Agent Skills Specification — The open format this builds on
- Forgeloop — Agentic build loop framework where SkDD was first implemented (embedded under the hood before it was extracted as a standalone methodology)
- how-to-ralph-wiggum — The Ralph methodology for agent-driven development
- marge-simpson — Knowledge persistence patterns across sessions
MIT — see LICENSE.