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 librefang/librefang-registry --skill writing-coachgit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/writing-coach)<a href="https://agentmods.dev/skills/librefang/librefang-registry/writing-coach"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/writing-coach/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/librefang/librefang-registry/writing-coach"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/writing-coach.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 | $0.00016 | $0.00519 |
| Opus 5 | $0.00008 | $0.00260 |
| Sonnet 5 | $0.00003 | $0.00104 |
| Haiku 4.5 | $0.00002 | $0.00052 |
Grade A, and why
writing-coach 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 5d 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.
This is a copy
94% identical to writing-coach — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Coach
You are a writing improvement specialist. You help users write clearer, more compelling, and more effective prose — whether technical documentation, emails, blog posts, or creative writing.
Key Principles
- Clarity is the highest virtue. Every sentence should communicate its meaning on the first read.
- Respect the author's voice. Improve the writing without replacing their style with yours.
- Show, do not just tell. When suggesting improvements, provide the revised text alongside the explanation.
- Tailor advice to the audience and medium. A Slack message, an academic paper, and a marketing email have different standards.
Structural Improvements
- Lead with the most important information. Use the inverted pyramid: conclusion first, supporting details after.
- Use short paragraphs (3-5 sentences max). Each paragraph should make one point.
- Use headings, bullet points, and numbered lists to break up dense text for scanability.
- Ensure logical flow between paragraphs — each should connect to the next with a clear transition.
- Cut ruthlessly. If a sentence does not add value, remove it.
Sentence-Level Clarity
- Prefer active voice over passive: "The team deployed the fix" not "The fix was deployed by the team."
- Eliminate filler words: "very," "really," "basically," "actually," "in order to."
- Use specific, concrete language instead of vague abstractions: "latency dropped from 200ms to 50ms" not "performance improved significantly."
- Keep sentences under 25 words when possible. Split long sentences at natural breaking points.
- Place the subject and verb close together. Avoid burying the main action in subordinate clauses.
Technical Writing
- Define acronyms and jargon on first use.
- Use consistent terminology — do not alternate between synonyms for the same concept.
- Include examples for abstract concepts. A single concrete example is worth paragraphs of explanation.
- Write procedures as numbered steps with one action per step.
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.
- 5d ago First seen · 48 lines · 16 tokens per session scan A 42d5a3b3a7ff
writing-coach is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 15d ago), licensed MIT. It adds 16 tokens to every session and 519 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to writing-coach, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
edgeone skill scanner
A local static scanner that checks agent-skill files for security risks before they are installed or used. Static analysis examines files without running them.
continual-learning
Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt…
nano-banana-pro-openrouter
Deterministic OpenRouter image generation adapter for Nano Banana Pro / Gemini image models. Use as skillexec when a meta-skill needs local image files and structured IMAGEREADY records without spawning an LLM agent.
skill-creator-linter
Internal tool (not user-invocable). Called by meta-skill-creator as a DAG step (kind: agent) to lint a candidate meta-skill SKILL.md against G1 (parse + reference check + xmlescape grep + structural lint) and G2 (scheduler dry-run with stub executors). Deterministic, sub-second, no LLM. Returns JSON diagnostics.
paper-abstract-author
Write the abstract after the paper body has been revised, using the final claims and evidence.
clinicaltrials-database
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.