Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/me2resh/apexyardnpx agentmods add skills/me2resh/apexyard/extract-featuresWrote 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/me2resh/apexyard/extract-features)<a href="https://agentmods.dev/skills/me2resh/apexyard/extract-features"><img src="https://agentmods.dev/badge/skills/me2resh/apexyard/extract-features/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/me2resh/apexyard/extract-features"><img src="https://agentmods.dev/badge/skills/me2resh/apexyard/extract-features.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 | $0.00036 | $0.08999 |
| Opus 5.5 | $0.00014 | $0.03600 |
| Sonnet 5.5 | $0.00007 | $0.01800 |
| Haiku 4.5 | $0.00004 | $0.00900 |
Grade A, and why
extract-features 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 4d 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 — 598 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing: read docs/skills/shared/writing-rule.md before you write a durable artifact.
/extract-features — Feature Inventory for Greenfield Rewrites
Walks the target project's codebase across six discovery axes and writes a consolidated Feature Inventory. The artefact is the "what we must preserve" specification for a greenfield rewrite (different language, framework, or architecture) — instead of reverse-engineering features one route at a time, hand the inventory to the rewrite team.
This skill complements /handover:
/handoverproduces a high-level project assessment — origin, current health, integration plan, applicable roles. The output is the bridge between "we just inherited this codebase" and "this codebase is now governed by our normal SDLC"./extract-featuresproduces a granular feature catalogue — every route, model, job, test name, UI screen, and documented capability the existing system exposes. The output is the input to a greenfield-rewrite spec.
Run /handover first when adopting an unfamiliar repo; run /extract-features second when you've decided to rewrite it.
See also: /feature-diagram <feature-slug> — once the inventory exists, this skill emits a per-feature Mermaid sub-graph (routes + models + jobs + screens for one feature) at projects/<name>/features/<slug>.md. The inventory's Feature column gains a link to each per-feature diagram. Sibling to /c4 (system topology) and /dfd (data flows) in the architecture-doc family — different lens (per-feature slice) on the same codebase. See AgDR-0035 for the design rationale.
LSP-aware (optional, recommended)
Discovery walks across all six axes — documentSymbol, references, definition queries — are the obvious win for LSP. With ENABLE_LSP_TOOL=1 + per-language plugin (per docs/getting-started.md § "Optional: LSP-aware code navigation"), route-handler enumeration, model walks, and test-name extraction are ~3-15× cheaper in token cost than grep + Read. Without LSP, the skill falls back to grep transparently using the framework signatures listed below. No new failure mode, just optional speed.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago Changed · -2 lines 42cde68a0fd5
- 1mo ago Changed · +4 lines 89fd5c6d9dcc
- 1mo ago First seen · 596 lines · 36 tokens per session scan A 120672125db0
extract-features is a skill published in the GitHub repository me2resh/apexyard (533 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 8,999 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.
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