plugins/faos-tech-writer/skills/doc-coauthoring/SKILL.md
<!-- AUTO-GENERATED by export-plugins.py — DO NOT EDIT --> --- name: doc-coauthoring description: Structured collaborative document creation through context gathering, iterative refinement, and reader testing. Use when co-authoring documentation, proposals, technical specs, decision docs, or RFCs. tags: [documentation, writing, collaboration, specs] --- # Doc Co-Authoring Workflow This skill provides a structured workflow for guiding users through collaborative document creation. Act as an act
npx skillsauth add frank-luongt/faos-skills-marketplace plugins/faos-tech-writer/skills/doc-coauthoringInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
Trigger conditions:
Initial offer: Offer the user a structured workflow for co-authoring the document. Explain the three stages:
If user declines, work freeform. If user accepts, proceed to Stage 1.
Goal: Close the gap between what the user knows and what Claude knows, enabling smart guidance later.
Start by asking the user for meta-context about the document:
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:
If user mentions editing an existing shared document:
Once initial questions are answered, encourage the user to dump all the context they have. Request information such as:
Advise them not to worry about organizing it - just get it all out.
During context gathering:
If user mentions entities/projects that are unknown:
As user provides context, track what's being learned and what's still unclear
Asking clarifying questions:
When user signals they've done their initial dump, ask clarifying questions:
Generate 5-10 numbered questions based on gaps in the context.
Inform them they can use shorthand to answer (e.g., "1: yes, 2: see #channel, 3: no because backwards compat").
Exit condition: Sufficient context has been gathered when questions show understanding - when edge cases and trade-offs can be asked about without needing basics explained.
Goal: Build the document section by section through brainstorming, curation, and iterative refinement.
For each section:
Ask 5-10 clarifying questions about what should be included in the section.
Brainstorm 5-20 things that might be included, depending on section complexity. Look for:
Ask which points should be kept, removed, or combined. Request brief justifications.
Ask if there's anything important missing for the section.
Draft the section based on curated selections.
As user provides feedback:
Continue iterating until user is satisfied with the section.
After 3 consecutive iterations with no substantial changes, ask if anything can be removed without losing important information.
As approaching completion (80%+ of sections done), re-read the entire document and check for:
Goal: Test the document with a fresh Claude (no context bleed) to verify it works for readers.
Generate 5-10 questions that readers would realistically ask.
For each question, invoke a sub-agent with just the document content and the question.
Summarize what Reader Claude got right/wrong for each question.
Invoke sub-agent to check for ambiguity, false assumptions, contradictions.
If issues found, loop back to refinement for problematic sections.
When Reader Claude consistently answers questions correctly and doesn't surface new gaps or ambiguities, the doc is ready.
When Reader Testing passes:
development
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development
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development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: gptq description: GPTQ post-training quantization for generative models. Use when quantizing large models to 4-bit with calibration-based weight compression. --- # GPTQ (Generative Pre-trained Transformer Quantization) Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization. ## When to use GPTQ **Use GPTQ when:** - Need to fit large models (70B+) on limited GPU