Borrowing it
Nothing to install: this file belongs to DataCovey/nornweave. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DataCovey/nornweave/main/.cursor/skills/doc-coauthoring/SKILL.mdgit clone --depth 1 https://github.com/DataCovey/nornweaveWrote 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/datacovey/nornweave/doc-coauthoring)<a href="https://agentmods.dev/skills/datacovey/nornweave/doc-coauthoring"><img src="https://agentmods.dev/badge/skills/datacovey/nornweave/doc-coauthoring/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/datacovey/nornweave/doc-coauthoring"><img src="https://agentmods.dev/badge/skills/datacovey/nornweave/doc-coauthoring.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 | $0.00077 | $0.03304 |
| Opus 5 | $0.00039 | $0.01652 |
| Sonnet 5 | $0.00015 | $0.00661 |
| Haiku 4.5 | $0.00008 | $0.00330 |
Grade A, and why
doc-coauthoring 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 10d 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
89% identical to doc-coauthoring — 46 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 — 376 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Doc Co-Authoring Workflow
This skill provides a structured workflow for guiding users through collaborative document creation. Act as an active guide, walking users through three stages: Context Gathering, Refinement & Structure, and Reader Testing.
When to Offer This Workflow
Trigger conditions:
- User mentions writing documentation: "write a doc", "draft a proposal", "create a spec", "write up"
- User mentions specific doc types: "PRD", "design doc", "decision doc", "RFC"
- User seems to be starting a substantial writing task
Initial offer: Offer the user a structured workflow for co-authoring the document. Explain the three stages:
- Context Gathering: User provides all relevant context while Cursor asks clarifying questions
- Refinement & Structure: Iteratively build each section through brainstorming and editing
- Reader Testing: Test the doc with a fresh LLM agent (no context) to catch blind spots before others read it
Explain that this approach helps ensure the doc works well when others read it (including when they paste it into LLM agent). Ask if they want to try this workflow or prefer to work freeform.
If user declines, work freeform. If user accepts, proceed to Stage 1.
Stage 1: Context Gathering
Goal: Close the gap between what the user knows and what LLM agent knows, enabling smart guidance later.
Initial Questions
Start by asking the user for meta-context about the document:
- What type of document is this? (e.g., technical spec, decision doc, proposal)
- Who's the primary audience?
- What's the desired impact when someone reads this?
- Is there a template or specific format to follow?
- Any other constraints or context to know?
Inform them they can answer in shorthand or dump information however works best for them.
If user provides a template or mentions a doc type:
- Ask if they have a template document to share
- If they provide a link to a shared document, use the appropriate integration to fetch it
- If they provide a file, read it
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.
- 10d ago First seen · 376 lines · 77 tokens per session scan A 578b406553b2
doc-coauthoring is a skill published in the GitHub repository DataCovey/nornweave (27 stars, last pushed 9d ago), licensed Apache-2.0. It adds 77 tokens to every session and 3,304 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to doc-coauthoring, differing in 46 lines, and is treated as a copy.
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