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.
npx skills add samibs/skillfoundry --skill featuregit 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/skills/samibs/skillfoundry/feature)<a href="https://agentmods.dev/skills/samibs/skillfoundry/feature"><img src="https://agentmods.dev/badge/skills/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/skills/samibs/skillfoundry/feature"><img src="https://agentmods.dev/badge/skills/samibs/skillfoundry/feature.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00022 | $0.04751 |
| Opus 5.5 | $0.00009 | $0.01900 |
| Sonnet 5.5 | $0.00004 | $0.00950 |
| Haiku 4.5 | $0.00002 | $0.00475 |
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 — 554 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 · 554 lines · 22 tokens per session scan A c345931323b4
feature is a skill published in the GitHub repository samibs/skillfoundry (12 stars, last pushed 14d ago), licensed MIT. It adds 22 tokens to every session and 4,751 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.
close-task-commit-push-pr
Close the active backlog task (detected from branch name), commit all changes, push to remote, and open a pull request. Use when the user says "close task and ship it", "close task commit push pr", or invokes /close-task-commit-push-pr.
github-workflow
GitHub best practices for pull requests, code reviews, issues, Actions workflows, and repository management.
memorix-git-memory
Use when the task depends on commit history, what changed, when a fix shipped, or linking engineering evidence to reasoning memory.
ring:generating-pr-descriptions
Generating pull request descriptions from git branch changes with automatic title generation, change-type detection, and smart analysis. Uses branch-only scope to avoid full history analysis. Use when preparing a PR for review. Skip when the PR is a single trivial commit or description already exists.
lerna
Best practices for Lerna monorepo management, versioning, and publishing.