agent-device-lab
Prepare and check trusted macOS hosts for physical Android and iPhone tests with agent-device, ADB, and Xcode, or set up iPhone…
Build, review, and audit technical docs and agent instruction files. Applies Simplified Technical English (ASD-STE100) to prose, scales to huge docs trees with sharded audits and ledgers, and runs as Claude Workflows or sub-agents when available.
$ npx -y skills add vincentkoc/dotskills --skill technical-documentation --agent claude-codeHow it fires
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/technical-documentationContext preview
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Build, review, and audit technical docs and agent instruction files. Applies Simplified Technical English (ASD-STE100) to prose, scales to huge docs trees with sharded audits and ledgers, and runs as Claude Workflows or sub-agents when available.
name: technical-documentation description: Build, review, and audit technical docs and agent instruction files. Applies Simplified Technical English (ASD-STE100) to prose, scales to huge docs trees with sharded audits and ledgers, and runs as Claude Workflows or sub-agents when available. license: MIT metadata: source: "https://github.com/vincentkoc/dotskills"
Produce, review, and audit technical documentation that is clear, unambiguous, and maintainable for both humans and agents. Covers product docs, contributor-governance files, and agent instruction files. Prose follows Simplified Technical English so a reader or an agent cannot misread it. Audits scale to trees with hundreds of pages through sharding, adversarial checks, and parallel remediation.
1. Classify the task as `build`, `review`, `audit`, or `rewrite`, and the context as `brownfield` or `evergreen`. 2. Inventory the requested file, directory, or diff scope early. Include governance and product docs for documentation-wide work. Repository size alone does not expand a scoped review. 3. Detect multilingual scope and define the required parity level. 4. Read `references/agent-and-contributing.md` for agent instruction and `CONTRIBUTING.md` rules. 5. Read `references/principles.md` for the governing ruleset (Matt Palmer, OpenAI, and ASD-STE100). 6. Read `references/simplified-technical-english.md` before writing or rewriting any prose. Pick Strict or STE-flavored mode per file. 7. Use `sub-agent-assisted` when delegation helps and the Agent tool exists, otherwise use `single-agent`. Read `references/workflows.md` when the scope needs sharding or the user requests a Workflow. Use `workflow` only with user opt-in and an available Workflow tool. 8. For build tasks, follow `references/build.md`. 9. For review tasks, follow `references/review.md` and detect issues proactively. 10. When the requested audit scope needs sharding, follow `references/large-docs-audit.md`. Run the round-0 mechanical scans and the repo's native validators first, then shard by character budget and audit every page in scope in full. Batch adversarial checks per shard. Run further rounds until two add nothing new, and report the count each round added rather than claiming convergence. Never cut an audit short to fit a message. 11. For rewrite tasks, run `scripts/ste-lint.py` first, rewrite per the STE process, and re-run the lint. Hard violations must not increase. Return the rewritten text alone unless the user asks for the rule table. 12. For remediation at scale, group the ledger into PRs with `references/pr-program.md` before editing anything, then land them in phase order. 13. Use `references/tooling.md` when platform/tooling choices affect recommendations. 14. Run a proactive issue sweep within the requested scope. Fix high-confidence defects in the same pass unless asked for report-only mode. 15. In brownfield mode, prioritize compatibility with current docs IA, tooling, and release state. In evergreen mode, prioritize timeless wording and durable structure. 16. Return deliverables plus validation notes, coverage, parity status, and remaining gaps.
Prefer delegation when the requested work is broad. Use it by default for repo-wide, multi-framework, or high-conflict work.
We are moving from "prompt as text" to skill as runtime module. Each skill acts like a lightweight, containerized application for AI work: a stable interface, opinionated workflow, and bundled resources that can be installed, versioned, tested, and reused
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