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 agentmods add skills/swingerman/engineer/feature-initnpx skills add swingerman/engineer --skill feature-initgit clone --depth 1 https://github.com/swingerman/engineerWhat 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 | $0.00052 | $0.02247 |
| Opus 5 | $0.00026 | $0.01123 |
| Sonnet 5 | $0.00010 | $0.00449 |
| Haiku 4.5 | $0.00005 | $0.00225 |
Grade A, and why
feature-init 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 2d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
feature-init
Create a feature folder for the DAE pipeline — feature.md (the Ready contract), the folder, a branch, and a tracker entry. Checkpoint 1.5.
When to use
Five paths:
- From
discuss— receives afeature_intakepayload in context (park or promote outcome). No interview. - Standalone — no payload; run the intake interview.
- From a tracker capture — promoting an untriaged row a human added directly to the tracker (no
Slug; see Tracker-as-intake inreferences/tracker.md). Pre-fill intake from the row (its title →title/outcome,Type, any notes); confirm the rest. Reuse the row — don't create a new one (Step 9). - From a roadmap item — promoting a strategic roadmap item (status
planned) into a feature (typically fromnext's ON THE ROADMAP bucket, ordiscuss). Pre-fill intake from the item (title,area,notes). Signalled by aroadmap_ref(the itemid) passed in — carry it ontofeature.mdand mark the item in-progress (Step 9). See the roadmap ↔ feature funnel inreferences/roadmap.md. - Onboarding intake — invoked by
onboard(or directly) to formalize work that already exists in the codebase. The feature folder is reverse-engineered from an existing spec / branch / implementation. Enters atstatus: in-progressordone, notready.
Detect from-discuss vs standalone by whether a feature_intake payload is present; a tracker capture is signalled by a slug-less tracker_ref passed in (typically from next); a roadmap promotion is signalled by a roadmap_ref (item id) passed in; onboarding intake is signalled by onboard (or an explicit "formalize existing feature" request).
Not for: brand-new ideas worth exploring first (discuss), or editing an existing feature (feature-edit).
Workflow
Before the steps below, create one TodoWrite todo per workflow step (the full
list up front, as a roadmap). After Step 6 creates the feature folder, show the
pipeline breadcrumb: run
${CLAUDE_PLUGIN_ROOT}/scripts/dae_progress.py <feature-dir> and present its
output — for a just-initialized feature (no progress.md yet) it renders the
pipeline ahead. The breadcrumb is advisory and never blocks. See
${CLAUDE_PLUGIN_ROOT}/references/progress-indicator.md.
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.
- 2d ago First seen · 56 lines · 52 tokens per session scan A 5cc353e8561d
feature-init is a skill published in the GitHub repository swingerman/engineer (144 stars, last pushed 6d ago), licensed MIT. It adds 52 tokens to every session and 2,247 once invoked, about $0.0003 per session on Opus 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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