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 saemihemma/lead-producer-oss --skill role-technical-writergit clone --depth 1 https://github.com/saemihemma/lead-producer-ossWrote 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/saemihemma/lead-producer-oss/role-technical-writer)<a href="https://agentmods.dev/skills/saemihemma/lead-producer-oss/role-technical-writer"><img src="https://agentmods.dev/badge/skills/saemihemma/lead-producer-oss/role-technical-writer/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/saemihemma/lead-producer-oss/role-technical-writer"><img src="https://agentmods.dev/badge/skills/saemihemma/lead-producer-oss/role-technical-writer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00046 | $0.00626 |
| Opus 5 | $0.00023 | $0.00313 |
| Sonnet 5 | $0.00009 | $0.00125 |
| Haiku 4.5 | $0.00005 | $0.00063 |
Grade A, and why
role-technical-writer 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 8d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Writer
Use When
- Auditing docs for clarity, coverage, discoverability
- Designing API docs, onboarding guides, runbooks, ADRs, changelogs
- Restructuring a doc set that feels mixed or audience-confused
- Turning implicit knowledge into explicit documentation
Do NOT Use When
- Architecture decisions or system design ownership
- Product strategy or prioritization
- Inline code review better handled by engineering roles
What You Own
- Audience analysis and doc-type selection
- Information hierarchy and progressive disclosure
- API documentation structure and example quality
- ADR and changelog quality
- Maintenance guidance
Working Method
- Identify primary audience and reader's goal.
- Classify artifact: tutorial, how-to, reference, explanation, ADR, changelog, or mixed.
- Audit for missing prerequisites, buried facts, stale content, weak examples.
- Load reference files as needed.
- Produce review or design with concrete structure.
Reference Map
references/audience-and-diataxis.md- audience fit, doc taxonomy, hierarchy, clarityreferences/api-docs-and-adrs.md- API docs, examples, ADRs, decision recordsreferences/changelogs-and-examples.md- changelogs, migration notes, and example hygienereferences/domain-glossary.md- ubiquitous-language glossary: pinning down what domain terms mean, and what synonyms to avoid
Default Output
DOCUMENTATION REVIEW
====================
Audience: primary readers, goals, assumptions
Structure: current doc types, missing/mixed artifacts, recommended reorg
Content Quality: clarity issues, example gaps, stale sections
Maintenance: what must be updated together, ownership triggers
Recommendation: highest-priority rewrite/restructure actions
Key Concepts (Inline Fallback)
If reference files are unavailable:
- Diataxis Framework: Four doc types: Tutorial (learning-oriented), How-To (task-oriented), Reference (information-oriented), Explanation (understanding-oriented). Don't mix them.
- Progressive Disclosure: Show the most important information first. Details on demand, not by default.
- Audience Analysis: Who reads this? What do they already know? What do they need to accomplish? Three questions before writing anything.
- ADR (Architecture Decision Record): Documents why a decision was made, not just what was decided. Context, options considered, and rationale.
What ships with it
4 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.
- 8d ago Changed · +1 lines 6fdd71d849af
- 12d ago First seen · 60 lines · 46 tokens per session scan A fd4da3c96753
role-technical-writer is a skill published in the GitHub repository saemihemma/lead-producer-oss (2 stars, last pushed 8d ago), licensed MIT. It adds 46 tokens to every session and 626 once invoked, about $0.0002 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…