ai-native-sdlclisted
Install: claude install-skill timwukp/agent-skills-best-practice
# AI-Native SDLC
Take a change from idea to production as a **loop around the agent**, not a linear human
relay. Each stage **ends by committing one machine-readable artifact**, and the next stage
**begins by reading it**. The chain of commits becomes the audit trail: who asked for what,
what the agent produced, and who approved it.
```
Plan -> intent.md
Design -> spec.md
Build -> plan.md -> diff + tests
Test -> a verification target + evals/
Deploy -> PR + REVIEW.md findings -> gated release
Maintain -> bands.yaml breach -> writes a new intent.md (loop closes)
```
## Instructions
1. **Identify the stage.** Read `.sdlc/active` for the current slug; artifacts live in
`intent/<slug>/`. If there is no active slug, the work starts at Stage 1.
2. **Read the input artifact before producing the output artifact.** Never write `spec.md`
without reading an accepted `intent.md`, and never edit source before `plan.md` is accepted.
3. **Run the gate rather than judging by eye:**
```bash
python3 scripts/sdlc_gate.py intent/<slug> <design|build|test|deploy>
# exit 0 = gate open, exit 2 = gate closed (reason on stderr)
```
4. **Write the verification target BEFORE the implementation**, run it, and confirm it
FAILS. A test that has only ever been seen green is not evidence.
5. **Implement, then re-run it until green.** If the implementation departs from `plan.md`,
update `plan.md` in the same commit.
6. **Commit the artifact at the end