Project Discovery logo

Project Discovery

Organization
testdouble
project-discovery

Discovers the core attributes of the current code repository and its projects — languages, frameworks, tooling, and where things live — and writes a concise reference section directly into the project's AGENTS.md or CLAUDE.md for other skills, agents, and hooks to consume. Use when scanning, analyzing, or detecting the project's technology stack, build tools, or repository structure. Does not create or update project documentation — use project-documentation for writing feature or system docs.

Overview

Publishertestdouble
Repositoryhan
Skill nameproject-discovery
Stars
320
Forks
26
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Project Discovery 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/testdouble/han/tree/main/han-core/skills/project-discovery
  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/testdouble/han.git /tmp/han
mkdir -p .claude/skills
cp -r /tmp/han/han-core/skills/project-discovery .claude/skills/project-discovery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Project Discovery 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 Project Discovery 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 Project Discovery 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.

Project Context

  • Default branch: !git symbolic-ref --short refs/remotes/origin/HEAD 2>/dev/null || echo unknown
  • AGENTS.md: !find . -maxdepth 1 -name "AGENTS.md" -type f
  • CLAUDE.md: !find . -maxdepth 1 -name "CLAUDE.md" -type f
  • README: !find . -maxdepth 1 -name "README*" -type f
  • personal config directory: !bash "${CLAUDE_PLUGIN_ROOT}/scripts/han-config-dir.sh" 2>/dev/null || echo "$HOME/.claude"
  • project .han/config.md: !cat .han/config.md 2>/dev/null || echo ""

As your first action, use the Read tool on .han/config.md inside the personal config directory path above. A read that returns no file is no personal configuration: continue silently. When that file or the project .han/config.md probe supplies content, apply it per config-rule.md, which governs precedence between the two files, relative-path resolution, and what to do with a file that reads but cannot be used.

Project Discovery

This skill discovers the project's core attributes and writes them as a concise ## Project Discovery section directly into the project's AGENTS.md or CLAUDE.md — not a separate file. The output is small by design: a few notes on where things live, the languages and frameworks, and the commands to run, so an AI agent working in the repo can find its way around. It is not an exhaustive inventory.

Step 1: Choose and read the target file

Pick the single file this discovery is written into, by priority:

  1. If AGENTS.md exists (the label above is non-empty), the target is AGENTS.md.
  2. Otherwise, if CLAUDE.md exists, the target is CLAUDE.md.
  3. If neither exists, the target is a new CLAUDE.md at the repository root, which you will create in Step 4.

If the target file already exists, read it in full and build the deduplication baseline: a list of everything the file already documents that this skill would otherwise write — directory and folder locations, languages, frameworks, package manager, build/test/lint/dev commands, and the docs, ADR, and coding-standards directories. Note whether the file already has a ## Project Discovery section.

If the target is a new CLAUDE.md, there is nothing to deduplicate against.

Step 2: Discover repository structure

Launch a han-core:project-scanner agent to determine whether the repository contains one project or many, and what each project's boundaries are. Wait for the agent to complete.

From the agent's results, build a project list. Each entry has a project name (directory name, or repository name for a root-level project), root path, and dependency manifest path.

Step 3: Explore project attributes

Launch 3 han-core:project-scanner agents in parallel, each with a different focus area. Include the project list from Step 2 in each agent's prompt so they know which roots to explore. Keep each agent on the core facts an AI agent needs to navigate and run the project — not an exhaustive catalog of every config file.

Agent 1 — Languages and Frameworks: For each project, read the dependency manifest to identify languages and version constraints. Determine the package manager from the lock file type. From dependencies, identify the structural/architectural frameworks that define how the project is built (web, frontend, test, ORM/database). Ignore utility packages. Note runtime version requirements.

Agent 2 — Commands: For each project, find the task runner or build definition and extract the actual commands for installing dependencies, running tests, linting, building, and the dev server. Only record commands that actually exist — no guesses.

Agent 3 — Layout: Map where the important things live: the main source directory or directories, the test location, and the documentation, ADR, and coding-standards directories — do not assume names like "docs". Capture only the handful of directories someone needs to know to find their way around the repo; skip incidental files.

After all 3 agents complete, merge their findings, deduplicate across agents, and organize by project. Separate repository-level items (default branch, docs, ADRs, coding standards, layout) from project-level items (language, frameworks, package manager, commands).

Step 4: Write the discovery into the target file

Build a concise ## Project Discovery section using the template at template.md as the structural guide.

Apply two filters before writing anything:

  • Deduplicate. Drop every fact already present in the target file (the Step 1 baseline). Do not restate what the file already says, even in different words.
  • Drop empties. Omit any line with no discovered value. Never leave a {placeholder} behind, and never invent a command or path that was not discovered.

If a discovered fact contradicts what the file already states (for example, the file says make test but no Makefile was discovered), use AskUserQuestion to surface the contradiction and ask which is correct — the existing file or the filesystem discovery. Update the content based on the answer.

Then write the result into the target file:

  • Target exists, no ## Project Discovery section: append the section at the end of the file (or another sensible location).
  • Target exists, already has a ## Project Discovery section: replace that section's body with the new, deduplicated content. Do not leave the old content alongside the new.
  • Neither AGENTS.md nor CLAUDE.md exists: create CLAUDE.md at the repository root containing the section.

If, after deduplication, nothing meaningful remains to add, do not write an empty section. Tell the user the target file already covers the project's core attributes, and stop.

Step 5: Keep the .han/config.md pointer honest

Han skills read project-local overrides from the project's .han/config.md when a project carries one (see ../../references/config-rule.md). This step keeps the target file's pointer to that config accurate, using the same consent gate and deduplication discipline as Step 4. The Project Context probe above shows whether the file exists.

  • .han/config.md exists and the target file contains no reference to it: use AskUserQuestion to offer adding a one-line pointer beside the ## Project Discovery section, for example: Han skills in this project read overrides from [.han/config.md](./.han/config.md). Never add a pointer when any reference to the file is already present, even phrased differently.
  • .han/config.md does not exist but the target file still references it: use AskUserQuestion to offer removing the stale pointer.
  • Otherwise: do nothing and say nothing about the config.

Write or remove the pointer only with the user's consent.

Step 6: Verification

Read back the target file's ## Project Discovery section. Confirm: no {placeholder} text remains, every bullet has a real discovered value, and nothing duplicates content stated elsewhere in the file. Spot-check 2-3 discovered paths or directories with Glob to confirm they exist.

Report to the user: the target file written (and whether it was created or updated), the number of projects discovered, and the languages and frameworks found.

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 Project Discovery AI skill do?

Discovers the core attributes of the current code repository and its projects — languages, frameworks, tooling, and where things live — and writes a concise reference section directly into the project's AGENTS.md or CLAUDE.md for other skills, agents, and hooks to consume. Use when scanning, analyzing, or detecting the project's technology stack, build tools, or repository structure. Does not create or update project documentation — use project-documentation for writing feature or system docs.

Why use Project Discovery on TypingMind?

Because you install it once and use it with any model. Project Discovery 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 Project Discovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/testdouble/han/tree/main/han-core/skills/project-discovery. 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 Project Discovery?

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 Project Discovery?

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

Is the Project Discovery AI skill free?

Yes. It is published on GitHub by testdouble 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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