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 nonlinear-xyz/factory-kit --skill factory-feature-architectgit clone --depth 1 https://github.com/nonlinear-xyz/factory-kitWrote 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/nonlinear-xyz/factory-kit/factory-feature-architect)<a href="https://agentmods.dev/skills/nonlinear-xyz/factory-kit/factory-feature-architect"><img src="https://agentmods.dev/badge/skills/nonlinear-xyz/factory-kit/factory-feature-architect.svg" alt="Measured on agentmods" 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 | $0.00098 | $0.01457 |
| Opus 5 | $0.00049 | $0.00728 |
| Sonnet 5 | $0.00020 | $0.00291 |
| Haiku 4.5 | $0.00010 | $0.00146 |
Grade A, and why
factory-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 7d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply the feature-architect specialist workflow. Turn a vague client request into a buildable specification, identify decisions, and route to the right specialist workflow. Do not write code. Load factory-stack end-to-end through the host's skill capability when needed.
How to think (in order)
-
What is the user actually asking? Restate the request in one sentence. If ambiguous, name the ambiguity and pick the most reasonable interpretation — don't ask follow-up questions; commit and flag.
-
What's the noun? Map to one of:
- Entity — has a list, detail, create/edit (e.g. customers, invoices, products)
- Workflow — process spanning multiple entities (e.g. approval flow, claim verification)
- Dashboard / report — read-only aggregation
- Integration — external system in/out (CRM, accounting, ERP)
- AI / LLM feature — agent, RAG, chat
- Data pipeline — ingestion, transform, simulation
If it's not one of these, that's the finding — surface it.
-
What's the scope? Strict minimum-viable cut. If the user asks for "a customer management page," the cut might be:
- MVP: list + create + edit, no delete, no bulk ops, no filtering beyond status
- V2: filtering, search, soft delete
- V3: bulk actions, custom fields, exports
Name what's in MVP and what's deferred. Don't sneak V2 features into MVP.
-
Which decision-criteria choices apply? Reference
factory-stack.md. Common ones:- Component library: Mantine vs shadcn — pick based on project type + existing decisions
- API style: server actions vs tRPC — pick based on project pattern
- Auth provider: Better Auth + orgs / Supabase + RLS / Clerk
- ORM: Drizzle / Supabase types
- Storage: structured columns / JSONB envelope
If the project's
DECISIONS.mdexists, defer to it. If not, propose values inline. -
Which skills / subagents will this need? Route:
- UI surface →
frontend-engineer+factory-frontend.md - Forms with sensitive data →
frontend-engineer+factory-security.md - DB schema or migrations →
db-schema-architect(Phase B) +factory-data-layer.md - Auth wiring →
auth-wiring-specialist(Phase B) +factory-auth.md - LLM workflow →
llm-workflow-engineer+factory-llm-workflows.md - CSV / Python service / pipeline →
data-pipeline-engineer+factory-data-pipelines.md - Threat-model or AI-code review →
security-engineer+factory-security.md
- UI surface →
-
What are the risks? Surface concrete risks, not abstract concerns:
- Sensitive data → KMS at rest? BAA?
- Multi-tenant → org keying enforced at middleware + DB?
- High-volume mutation → rate limit? audit log?
- AI-code path → read-only-by-default? review queue?
-
What's the smallest correct change? If a request implies a redesign of something else, name it and stop — don't sneak it in.
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.
- 7d ago First seen · 120 lines · 98 tokens per session scan A 43b595913d39
factory-feature-architect is a skill published in the GitHub repository nonlinear-xyz/factory-kit (9 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 1,457 once invoked, about $0.0005 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.
Other skills, from other repositories
hook-template
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skill-template
Generate custom skill/command from template. Use when creating a new skill or slash command from scratch, or scaffolding a skill file with correct frontmatter.
check-environment
Verify Claude, Codex, and Grok availability plus Director guidance, relay, agents, and skills. Audit optional hooks only when selected. Use after installation or when a native surface misbehaves.
doc-writer
Documentation templates and standards: README structure, API reference format, changelog (Keep a Changelog), and comment guidelines. Use when creating or updating documentation. Loaded automatically by the doc-writer agent.
test-runner
Test execution reference: framework detection (pytest/jest/vitest/go/cargo/junit) and correct run/coverage commands, plus failure-analysis steps. Use when running tests, analyzing test failures, or verifying coverage after code changes.
agents
List all available agents (core, expert, self-evolving). Use when the user asks what agents are available or runs /agents.