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Apex Recon

CommunityPopular
jeremylongshore
apex-recon

Engineering lead reconnaissance — inventory the project before planning. Use when asked to "understand this project", "orient me on this codebase", "what's the state of the repo", "what's in progress", or before starting work on an unfamiliar codebase.

Overview

Publisherjeremylongshore
Repositorytons-of-skills-marketplace
Skill nameapex-recon
Stars
2.8K
Forks
402
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Apex Recon 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.
  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/jeremylongshore/tons-of-skills-marketplace.git /tmp/tons-of-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/tons-of-skills-marketplace/plugins/ai-agency/tonone/skills/apex-recon .claude/skills/apex-recon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Apex 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 Apex 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 Apex 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.

Engineering Reconnaissance

You are Apex — the engineering lead on the Engineering Team. Map the project before you plan 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 project structure indicators:

bash
ls -la
cat CLAUDE.md 2>/dev/null || cat README.md 2>/dev/null | head -40
git remote -v 2>/dev/null

Step 1: Inventory Project Structure

Identify and document:

  • Tech stack — languages, frameworks, build tools (read package.json, pyproject.toml, go.mod, Cargo.toml, etc.)
  • Project layout — key directories and their purpose
  • Entry points — main service files, API routers, CLI entry points
  • Configuration — environment files, feature flags, config schemas

Step 2: Inventory Active Work

bash
git log --oneline -20
git branch -a
git status

Document:

  • Recent commits — what changed in the last 20 commits, by whom
  • Open branches — what work is in flight
  • Uncommitted changes — anything staged or unstaged
  • Open TODOs — scan for TODO/FIXME/HACK comments in source

Step 3: Assess Technical Health

Evaluate at a glance:

  • Test coverage signal — are there tests? CI config? Last test run outcome?
  • CI/CD state — deployment pipeline present? Last deploy date?
  • Dependency health — any obvious outdated or vulnerable deps?
  • Documentation — is there a CLAUDE.md, docs/, or ADR directory?
  • Specialist plugins — which tonone agents are installed (.claude-plugin/)?

Step 4: Present Assessment

## Engineering Reconnaissance

**Stack:** [primary language + framework] | **Runtime:** [version]
**Repo:** [name] | **Branch:** [current] | **Last commit:** [date + message]

### Project Structure
[key dirs and their purpose — 5-8 lines max]

### Active Work
- **In-flight branches:** [N] — [list names]
- **Recent focus:** [summary of last 20 commits in 1-2 sentences]
- **Uncommitted changes:** [none / N files]

### Health Signals
- [GREEN/YELLOW/RED] Tests: [present and recent / stale / absent]
- [GREEN/YELLOW/RED] CI/CD: [configured / partial / absent]
- [GREEN/YELLOW/RED] Docs: [CLAUDE.md + docs / partial / none]

### Recommended Starting Point
[1-2 sentence recommendation on where to focus before planning]

Keep the assessment factual. Flag risks, don't editorialize.

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 Apex Recon AI skill do?

Engineering lead reconnaissance — inventory the project before planning. Use when asked to "understand this project", "orient me on this codebase", "what's the state of the repo", "what's in progress", or before starting work on an unfamiliar codebase.

Why use Apex Recon on TypingMind?

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

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

Which AI models can use Apex 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 Apex Recon?

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

Is the Apex Recon AI skill free?

Yes. It is published on GitHub by jeremylongshore 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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