Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/samibs/skillfoundryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/samibs/skillfoundry/feature)<a href="https://agentmods.dev/commands/samibs/skillfoundry/feature"><img src="https://agentmods.dev/badge/commands/samibs/skillfoundry/feature/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/samibs/skillfoundry/feature"><img src="https://agentmods.dev/badge/commands/samibs/skillfoundry/feature.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.04720 |
| Opus 5.5 | $0.00000 | $0.01888 |
| Sonnet 5.5 | $0.00000 | $0.00944 |
| Haiku 4.5 | $0.00000 | $0.00472 |
Grade A, and why
feature scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 548 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Lifecycle Orchestrator
Role: Per-feature quality pipeline that takes a story or feature description from zero to a committed, documented, tested, and evaluated implementation. No stage is skipped. No commit happens without the evaluator's approval.
Persona: See agents/feature-lifecycle.md for full persona definition.
Activation Syntax
/feature Prompt for story or description
/feature STORY-001 Run lifecycle for a specific story file
/feature "description of feature" Run lifecycle for an inline description
/feature --dry-run Preview pipeline without executing
/feature --from challenge Resume from a specific stage
/feature --no-commit Run full pipeline but don't commit
/feature --override "reason" Accept current gate failure with logged tradeoff
/feature --exec-mode advisory Force advisory mode (no shell-based test claims)
/feature --detect-stack Re-run stack detection before starting
/feature --lite Lite mode: no shadow tester, no Anvil, single-pass evaluator
Hard Rules
- NEVER commit before the evaluator gives ✅ or 🟡-resolved verdict
- NEVER skip the testloop — "it compiles" is not proof
- NEVER accept evaluator findings as informational — all 🔴 findings produce fix briefs that go back to the coder
- NEVER write documentation for code that has failing tests
- NEVER commit unrelated files — stage only the files touched by this feature
- ALWAYS track state across stages — a failed stage can be resumed
- STOP and escalate on evaluator verdict 🚫 (full rewrite) — human decision required
State File
All stage state is tracked in .claude/local/feature-state.json:
{
"story_id": "STORY-001",
"feature": "User authentication — JWT login flow",
"stage": "implement | testloop | challenge | document | commit | done | halted",
"status": "running | success | halted",
"started_at": "ISO8601",
"stages": {
"implement": { "status": "done", "files_modified": ["src/auth/auth.service.ts"] },
"testloop": { "status": "done", "iterations": 2, "final_pass_rate": "100%" },
"challenge": {
"cycles": 2,
"verdicts": ["🟡", "✅"],
"fix_briefs_sent": 1,
"final_verdict": "✅"
},
"document": { "status": "done", "files_updated": ["CHANGELOG.md", "docs/api_reference.md"] },
"commit": { "status": "done", "hash": "abc1234", "message": "feat(auth): JWT login flow [STORY-001]" }
}
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 548 lines · 0 tokens per session scan A 677fa7fa998c
feature is a command published in the GitHub repository samibs/skillfoundry (12 stars, last pushed 17d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,720 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
aqa-flow
Router workflow for test automation: routes to ui-aqa-flow, api-aqa-flow, or testgen-flow. Kept for backward compatibility.
dare-migrate
Camada semântica da migração (Fase 2 brownfield). Roda depois de dare migrate, que já leu os artefatos do reverse/dna e gerou os esqueletos. Sua função é escrever a estratégia de migração e os cenários Gherkin de paridade reais — o contrato comportamental que garante uma reimplementação fiel ao legado.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.