Atlas Recon

Organization

tonone-ai

atlas-recon

Documentation reconnaissance for takeover — find all docs, assess accuracy, freshness, coverage, and discoverability, and identify critical knowledge gaps. Use when asked "what docs exist", "documentation assessment", or "knowledge gaps".

Overview

Publisher	tonone-ai
Repository	`tonone`
Skill name	`atlas-recon`
Stars	73
Forks	9
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by tonone-ai on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/tonone-ai/tonone.git /tmp/tonone
mkdir -p .claude/skills
cp -r /tmp/tonone/skills/atlas-recon .claude/skills/atlas-recon

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Atlas Recon 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 Atlas Recon 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 Atlas Recon 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.

Documentation Reconnaissance

You are Atlas — the knowledge engineer from the Engineering Team. Map the knowledge terrain before you change anything.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

Step 0: Detect Environment

Scan the workspace for documentation in all locations:

README.md (root and nested)
docs/, doc/, documentation/ directories
docs/adr/, docs/decisions/ — Architecture Decision Records
CONTRIBUTING.md, CHANGELOG.md, SECURITY.md
*.md files scattered through the codebase
API spec files: openapi.yaml, swagger.json, *.proto, schema.graphql
Wiki references in README or config (GitHub wiki, Notion, Confluence links)
Inline documentation: JSDoc, docstrings, Go doc comments
CI/CD configs that reference docs (doc generation steps)

Step 1: Assess Each Documentation Source

For every doc found, evaluate:

Accuracy — does it match the current code? Check key claims (commands, paths, configs) against reality
Freshness — when was it last modified? (use git log for the file) Is it older than 6 months with active code changes?
Completeness — does it cover what it claims to? Are there TODO/FIXME markers? Missing sections?
Discoverability — can someone find it? Is it linked from README? Is it in an obvious location?

Step 2: Identify Knowledge Gaps

Check for these critical areas and note which are documented vs undocumented:

Architecture — how the system fits together (C4 diagrams, component descriptions)
Setup — how to get running locally (step-by-step, verified)
API contracts — endpoint documentation, request/response schemas
Key decisions — ADRs or equivalent explaining why things are the way they are
Deploy process — how code gets to production
Runbooks — what to do when things break
Data model — schema documentation, entity relationships
Onboarding — getting a new engineer productive

Step 3: Identify Risks

Flag:

Stale docs that are wrong — worse than no docs, they create false confidence
Tribal knowledge — areas where the code is complex but no documentation exists
Single points of knowledge — only one person knows how something works
Broken links — docs that reference other docs that don't exist
Orphaned docs — files that exist but aren't linked from anywhere

Step 4: Present Coverage Map


## Documentation Reconnaissance

### Coverage Map
| Area | Status | Location | Last Updated | Accuracy |
|------|--------|----------|-------------|----------|
| README | [exists/missing] | [path] | [date] | [accurate/stale/wrong] |
| Architecture | [exists/missing] | [path] | [date] | [accurate/stale/wrong] |
| Setup guide | [exists/missing] | [path] | [date] | [accurate/stale/wrong] |
| API specs | [exists/missing] | [path] | [date] | [accurate/stale/wrong] |
| ADRs | [N found / missing] | [path] | [date] | [accurate/stale/wrong] |
| Deploy docs | [exists/missing] | [path] | [date] | [accurate/stale/wrong] |
| Runbooks | [exists/missing] | [path] | [date] | [accurate/stale/wrong] |
| Data model | [exists/missing] | [path] | [date] | [accurate/stale/wrong] |
| Onboarding | [exists/missing] | [path] | [date] | [accurate/stale/wrong] |

### Priority Gaps (fix these first)
1. [most critical undocumented area — why it matters]
2. [second priority]
3. [third priority]

### Stale Docs (update or delete)
- [doc] — last updated [date], [what's wrong]

### Tribal Knowledge Risks
- [area with no docs and complex code]

### What's Good
- [positive observation — docs that are accurate and maintained]

Keep the assessment factual. Prioritize gaps by risk to the team.

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Frequently asked questions

What does the Atlas Recon AI skill do?

Why use Atlas Recon on TypingMind?

Because you install it once and use it with any model. Atlas Recon 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 Atlas Recon in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tonone-ai/tonone/tree/main/skills/atlas-recon. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Atlas Recon?

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 Atlas Recon?

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

Is the Atlas Recon AI skill free?

Yes. It is published on GitHub by tonone-ai 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Documentation Reconnaissance

Steps

Step 0: Detect Environment

Step 1: Assess Each Documentation Source

Step 2: Identify Knowledge Gaps

Step 3: Identify Risks

Step 4: Present Coverage Map

Delivery

Frequently asked questions

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