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Technical Documentation

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vincentkoc
technical-documentation

Build, review, and audit technical docs and agent instruction files. Applies Simplified Technical English (ASD-STE100) to prose, scales to huge docs trees with sharded audits and ledgers, and runs as Claude Workflows or sub-agents when available.

Overview

Publishervincentkoc
Repositorydotskills
Skill nametechnical-documentation
Stars
112
Forks
9
Bundled files
23
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 23 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by vincentkoc on GitHub. Read the source before you install it.

Installation

Install the Technical Documentation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vincentkoc/dotskills/tree/main/skills/technical-documentation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vincentkoc/dotskills.git /tmp/dotskills
mkdir -p .claude/skills
cp -r /tmp/dotskills/skills/technical-documentation .claude/skills/technical-documentation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Technical Documentation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Technical Documentation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Technical Documentation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Technical Documentation

Purpose

Produce, review, and audit technical documentation that is clear, unambiguous, and maintainable for both humans and agents. Covers product docs, contributor-governance files, and agent instruction files. Prose follows Simplified Technical English so a reader or an agent cannot misread it. Audits scale to trees with hundreds of pages through sharding, adversarial checks, and parallel remediation.

When to use

  • Creating or overhauling docs in an existing product/codebase (brownfield).
  • Building evergreen docs meant to stay accurate and reusable over time.
  • Reviewing doc diffs for structure, clarity, and operational correctness.
  • Running full-repo documentation audits across governance files and product docs surfaces (docs/, README*, .md/.mdx/.mdc, Fern/Sphinx/Mintlify/MkDocs sources).
  • Auditing a large or messy docs tree: hundreds of pages, pages over 20k characters, confusing navigation, many locales, poor reader experience.
  • Rewriting dense or ambiguous English into Simplified Technical English: procedures, reference pages, error messages, tool descriptions, AGENTS.md and CONTRIBUTING.md.
  • Updating or reviewing AGENTS.md and/or CONTRIBUTING.md to keep agent and contributor workflows aligned with current repo practices.
  • Designing governance documentation strategy for repos with alias instruction files (CLAUDE.md, AGENT.md, .cursorrules, .cursor/rules/*, .agent/, .agents/, .pi/).
  • Diagnosing agent-file drift where teams had to prompt iteratively to surface missing files, broken commands, or policy conflicts.

Evidence and artifacts

  • Keep routine scoped review and audit findings in chat or stdout. Do not create report files, ledgers, or shard directories by default.
  • Create durable evidence only for a requested deliverable or a concrete large-audit, resumption, or handoff requirement. State its consumer and purpose before writing files.
  • Reuse one task-owned location for required evidence. Preserve coverage, deduplication, journal, and recovery contracts when a large or resumable workflow needs them.
  • Report coverage and gaps whether findings stay inline or in files. Less persistence never means less investigation.

Workflow

  1. Classify the task as build, review, audit, or rewrite, and the context as brownfield or evergreen.
  2. Inventory the requested file, directory, or diff scope early. Include governance and product docs for documentation-wide work. Repository size alone does not expand a scoped review.
  3. Detect multilingual scope and define the required parity level.
  4. Read references/agent-and-contributing.md for agent instruction and CONTRIBUTING.md rules.
  5. Read references/principles.md for the governing ruleset (Matt Palmer, OpenAI, and ASD-STE100).
  6. Read references/simplified-technical-english.md before writing or rewriting any prose. Pick Strict or STE-flavored mode per file.
  7. Use sub-agent-assisted when delegation helps and the Agent tool exists, otherwise use single-agent. Read references/workflows.md when the scope needs sharding or the user requests a Workflow. Use workflow only with user opt-in and an available Workflow tool.
  8. For build tasks, follow references/build.md.
  9. For review tasks, follow references/review.md and detect issues proactively.
  10. When the requested audit scope needs sharding, follow references/large-docs-audit.md. Run the round-0 mechanical scans and the repo's native validators first, then shard by character budget and audit every page in scope in full. Batch adversarial checks per shard. Run further rounds until two add nothing new, and report the count each round added rather than claiming convergence. Never cut an audit short to fit a message.
  11. For rewrite tasks, run scripts/ste-lint.py first, rewrite per the STE process, and re-run the lint. Hard violations must not increase. Return the rewritten text alone unless the user asks for the rule table.
  12. For remediation at scale, group the ledger into PRs with references/pr-program.md before editing anything, then land them in phase order.
  13. Use references/tooling.md when platform/tooling choices affect recommendations.
  14. Run a proactive issue sweep within the requested scope. Fix high-confidence defects in the same pass unless asked for report-only mode.
  15. In brownfield mode, prioritize compatibility with current docs IA, tooling, and release state. In evergreen mode, prioritize timeless wording and durable structure.
  16. Return deliverables plus validation notes, coverage, parity status, and remaining gaps.

Sub-agent orchestration guidance

Prefer delegation when the requested work is broad. Use it by default for repo-wide, multi-framework, or high-conflict work.

  • inventory-agent -> agents/inventory-agent.md (haiku): file/config discovery, coverage map, missing-path checks.
  • ste-lint-agent -> agents/ste-lint-agent.md (haiku): deterministic STE lint baseline per shard.
  • governance-agent -> agents/governance-agent.md (sonnet): AGENTS/CONTRIBUTING/alias precedence, conflicts, policy drift.
  • docs-framework-agent -> agents/docs-framework-agent.md (sonnet): framework config, relative path base, file-path vs URL-path mapping.
  • verify-agent -> agents/verify-agent.md (sonnet): adversarial refutation of one finding.
  • docs-ux-audit-agent -> agents/docs-ux-audit-agent.md (fable): full-read reader-experience audit of one shard, ledger output.
  • ste-rewrite-agent -> agents/ste-rewrite-agent.md (fable): Strict or STE-flavored rewrite with meaning preserved.
  • remediation-agent -> agents/remediation-agent.md (fable): apply verified findings to one file, re-run validators.
  • synthesis-agent -> agents/synthesis-agent.md (fable): merge outputs into one prioritized fix plan.

With Claude Workflows (references/workflows.md), the audit template runs docs-ux-audit-agent per shard and one batched verify-agent per shard, then synthesis and a completeness critic. Per-finding verifier fan-out is the main way these runs exhaust a session, so keep verification batched and range-scoped. The remediation template runs remediation-agent per file with a reviewer behind it. Launch a Workflow only after the user opts in.

Inputs

  • Doc type (tutorial, how-to, reference, explanation) and audience.
  • File scope, directory scope, or diff scope.
  • Docs framework/tooling constraints (Fern, Mintlify, Sphinx, MkDocs, etc.).
  • Task mode (build, review, audit, rewrite) and brownfield/evergreen intent.
  • STE mode per file (strict or flavored), or let the skill infer from doc type.
  • Target agent and human compatibility intent.
  • Desired investigation depth (quick pass, exhaustive). Audits default to exhaustive within the requested scope.
  • Execution rung (workflow, sub-agent-assisted, single-agent) and Workflow opt-in.
  • Remediation mode (apply-fixes by default, or report-only).
  • Durable evidence purpose and path, only when required under Evidence and artifacts. Use .audit/ledger.jsonl at the audit root when a large-audit ledger is required.
  • Multilingual scope: source-of-truth language, target locales, parity expectations.

Outputs

  • Updated draft, rewritten text, or review findings with clear next actions.
  • Validation notes (what was checked, what remains), including STE lint before/after counts.
  • Coverage statement for audits: pages read out of pages in scope, per directory.
  • Findings with status counts (open, verified, refuted, fixed, wontfix), inline by default or in the required ledger.
  • Split plans for oversized pages and nav/IA change list.
  • Governance-doc alignment summary when AGENTS/CONTRIBUTING were touched.
  • Agent instruction-surface map (primary file, alias files, Codex/Claude/Cursor handling plan).
  • Documentation-surface coverage map within the requested scope (/docs, README hierarchy, framework source trees).
  • Delegation notes: agents or workflow run id used, scope delegated, how findings merged.
  • Round-by-round finding counts and a convergence statement.
  • PR program when the task is remediation at scale.
  • Multilingual parity note (in-sync, partial with rationale, or intentionally divergent).

Flow

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Technical Documentation AI skill do?

Build, review, and audit technical docs and agent instruction files. Applies Simplified Technical English (ASD-STE100) to prose, scales to huge docs trees with sharded audits and ledgers, and runs as Claude Workflows or sub-agents when available.

Why use Technical Documentation on TypingMind?

Because you install it once and use it with any model. Technical Documentation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Technical Documentation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vincentkoc/dotskills/tree/main/skills/technical-documentation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Technical Documentation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Technical Documentation?

As many as you like. As long as a model supports skills, you can use Technical Documentation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Technical Documentation AI skill free?

Yes. It is published on GitHub by vincentkoc under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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