Project Hardness

Community

appleweiping

project-hardness

Turn any local project into an AI-readable, maintainable, verifiable causal layer under the project's `.agent/` directory, and harden vague requirements into concrete task specs before editing. Use when (a) starting work on an unfamiliar or large project, (b) you need to know a change's blast radius / do-not-modify boundaries, or (c) a requirement is vague and you need clarified goal + involved modules + edit paths + risks + acceptance criteria + test commands. Not a grep/index/chat-memory; it is a static, source-backed project model. Applies to every agent (Claude Code, Codex, OpenCode, etc.).

Overview

Publisher	appleweiping
Repository	`AGENT_RESOURCE`
Skill name	`project-hardness`
Stars	78
Forks	10
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

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 appleweiping on GitHub. Read the source before you install it.

Installation

Install the Project Hardness 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.
https://github.com/appleweiping/AGENT_RESOURCE/tree/master/skills/vipin/project-hardness
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/appleweiping/AGENT_RESOURCE/tree/master/skills/vipin/project-hardness

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/appleweiping/AGENT_RESOURCE.git /tmp/AGENT_RESOURCE
mkdir -p .claude/skills
cp -r /tmp/AGENT_RESOURCE/skills/vipin/project-hardness .claude/skills/project-hardness

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

Use it in TypingMind

Enable Project Hardness 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 Hardness 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 Hardness 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-hardness

A per-project causal layer: a static, source-backed model of a project written to <project>/.agent/. It answers "what does this project do, what depends on what, what breaks if I change X, what must I not touch, and why was it built this way" — without re-reading the whole repo every session.

The engine lives in WEIPING_WIKI (scripts/hardness/, invoked via python scripts/wiki.py hardness). The artifacts live in the target project (.agent/, git-ignored by default). Only cross-project lessons are ever synced to agentmemory — project facts stay local.

When to use

Starting work on an unfamiliar, large, or long-dormant project → scan first.
About to change something and unsure of impact → impact <module> or read modules/<m>.md.
A requirement is vague → harden it into a task spec BEFORE writing code.
After completing work → update .agent/ (re-scan) and sync only reusable lessons.

Commands

Run from the root of a WEIPING_WIKI checkout:

bash
# 1. Build / refresh the causal layer for a project
python scripts/wiki.py hardness scan <project-path> [--name NAME] [--json]

# 1b. Discover and scan every important project on the machine (de-duped, safety-excluded)
python scripts/wiki.py hardness scan-all [--dry-run] [--json]

# 2. Harden a vague requirement into a concrete task spec (.agent/tasks/<id>.md)
python scripts/wiki.py hardness harden <project-path> "the requirement text" [--id ID]

# 3. Blast radius of a single module
python scripts/wiki.py hardness impact <project-path> <module-name>

# 4. Sync ONLY cross-project lessons to agentmemory (project facts are auto-rejected)
python scripts/wiki.py hardness sync <project-path> --lesson "general reusable lesson" [--commit]

sync is dry-run by default; pass --commit to actually send. It refuses any text that names concrete files, paths, or line numbers (those are project facts, not lessons).

What `.agent/` contains

Artifact	Purpose
`architecture.md`	Module table, responsibilities, dependency hotspots
`glossary.md`	Domain terms, entities, frameworks
`constraints.md`	Do-not-modify boundaries (secrets, lockfiles, migrations, markers)
`decisions.md`	Decision-flavored git history (manual additions preserved on regen)
`modules/*.md`	Per-module detail + blast radius + files
`flows/*.flow.md`	Business/process flows (route → handler → deps → entities)
`entities.json`	Entities and their lifecycle / touching modules
`causal-graph.json`	Full node/edge graph (imports, routes-to, reads/writes)
`tasks/*.md`	Hardened task specs
`index.json`	Manifest + provenance

Standard workflow

First contact: scan the project. Read architecture.md and constraints.md.
Per requirement: harden it. Read the generated tasks/<id>.md — it lists the clarified goal, involved modules, likely edit paths, risk points, acceptance criteria, and test commands. Refine it if the match confidence is LOW.
Before editing: check impact / the module's blast radius; never touch block constraints without explicit user approval.
After completing: re-scan to refresh .agent/; run the acceptance test commands; then sync only generalizable lessons to agentmemory.

Boundary rules (hard)

Project-specific facts stay in .agent/. Never write them to global agentmemory.
The engine never reads secret files (.env, *.key, credentials.*) — it records their existence as a block constraint and moves on.
.agent/ is git-ignored by default (it is a derived artifact, regenerable any time).
The engine is stdlib-only and fails gracefully if agentmemory is unreachable.

See the full design at wiki/concepts/project-hardness-system.md in WEIPING_WIKI.

Frequently asked questions

What does the Project Hardness AI skill do?

Why use Project Hardness on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/appleweiping/AGENT_RESOURCE/tree/master/skills/vipin/project-hardness. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Project Hardness?

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 Hardness?

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

Is the Project Hardness AI skill free?

Yes. It is published on GitHub by appleweiping under the Apache-2.0 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

project-hardness

When to use

Commands

What .agent/ contains

Standard workflow

Boundary rules (hard)

Frequently asked questions

Related AI Skills

Set up your own AI workspace now

What `.agent/` contains