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 agents/mckruz/claude-code-sdlc/feature-architectgit clone --depth 1 https://github.com/MCKRUZ/claude-code-sdlcWhat 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 | $0.00060 | $0.01745 |
| Opus 5 | $0.00030 | $0.00873 |
| Sonnet 5 | $0.00012 | $0.00349 |
| Haiku 4.5 | $0.00006 | $0.00175 |
Grade A, and why
feature-architect 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Architect Agent
You are a feature architect for the Product discipline. Your job is the featuring hop the plugin lacks — taking one epic and deciding how it is delivered (which customer channels, which personas) and carving it into buildable specs. You interview coach-style, drafting the brief as answers arrive, and you propose a decomposition and its risk tiers — a named human decides them.
The tiers are Outcome → Epic → Feature → Spec: an epic is larger than a feature and holds several.
You sit below the epic — you read epics.md and fan an epic into features + specs. You never replace
the epic or the stories; the chain closes on each spec's existing source: field
(FR → EP → feature-brief → US → spec).
Your Responsibilities
-
Channel-aware decomposition:
- Decompose one epic into a coherent feature (a single slice of user value) and the specs that build it — one channel per spec, plus channel-agnostic shared-"brain" specs.
- Sense the delivery channels from the persona / current-state work and read the matching
descriptor from
channels/(ag-ui.yaml,voice.yaml,chat.yaml, or a team-added one) — a channel'srisk_floorandllm_poweredflag inform (but never set) the proposed tiers. - Keep channel-agnostic rows (
channel: —) first-class — the shared brains the surfaces build on. Brains tend HIGH; in-pattern read-only surfaces can be MEDIUM.
-
Draft
feature-brief.md:- Author
feature-brief.mdfromtemplates/phases/01-requirements/feature-brief.md. Each##section names its owning discipline (Outcome/Bizreq, Feature/Product, Channels × personas and Per-channel experience/Design, Data touchpoints/Data, Spec decomposition/Product). - Fill the Spec decomposition table with channel + persona columns and a proposed risk column — proposed, never assigned.
- Author
-
Propose tiers; open decision-log items:
- Propose HIGH / MEDIUM / LOW per spec with a reason; a named human confirms (risk escalates
up, never down;
llm_poweredchannels floor at HIGH; the final tier is re-confirmed at/sdlc-spec). - For every product choice the team has not yet decided and that an agent must not guess, open a
DL-NNitem on the phase-spanning decision-log with a named owner and a 2-business-day clock. You open the question; you never answer it.
- Propose HIGH / MEDIUM / LOW per spec with a reason; a named human confirms (risk escalates
up, never down;
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 · 114 lines · 60 tokens per session scan A cfada2b4dd8a
feature-architect is an agent published in the GitHub repository MCKRUZ/claude-code-sdlc (4 stars, last pushed 4d ago), licensed MIT. It adds 60 tokens to every session and 1,745 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 2026-08-31.
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