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 organvm-iv-taxis/a-i--skills --skill doc-coauthoringgit clone --depth 1 https://github.com/organvm-iv-taxis/a-i--skillsWrote 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/organvm-iv-taxis/a-i--skills/doc-coauthoring)<a href="https://agentmods.dev/skills/organvm-iv-taxis/a-i--skills/doc-coauthoring"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/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/organvm-iv-taxis/a-i--skills/doc-coauthoring"><img src="https://agentmods.dev/badge/skills/organvm-iv-taxis/a-i--skills/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 13d 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
97% identical to doc-coauthoring — 5 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 — 381 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 Claude asks clarifying questions
- Refinement & Structure: Iteratively build each section through brainstorming and editing
- Reader Testing: Test the doc with a fresh Claude (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 Claude). 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 Claude 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 ships with it
2 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.
- 13d ago First seen · 381 lines · 77 tokens per session scan A ada839945e52
doc-coauthoring is a skill published in the GitHub repository organvm-iv-taxis/a-i--skills (17 stars, last pushed 16d 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 97% identical to doc-coauthoring, differing in 5 lines, and is treated as a copy.
Other skills, from other repositories
agent-communication-protocol
Open protocol for AI agent interoperability enabling standardized communication between agents, applications, and humans across different frameworks.
agent-creation-skill
Procedural guide for creating new agents from templates when project complexity requires delegation.
code-research
Use when produce structured understanding of unfamiliar codebases — architecture, data flows, dependencies, and conventions. Use when joining a new project, tracing feature implementations, or mapping system architecture.
market-research-agent
Use when analyze markets, competitors, user segments, and trends to produce evidence-based business intelligence. Use when evaluating market opportunities, pricing strategy research, or due diligence for investments. Use when analyzeing markets, competitors, user segments, and trends to produce evidence-based.
model-strategy
Multi-model orchestration and model-switching strategy. Score-based model selection, reasoning-effort routing, cross-agent delegation (Gemini, Codex, Ollama), advisor pairing, escalation triggers, permission matrix, and cost-efficiency optimization.
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.