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Nw Buddy Project Reading

Organization
nWave-ai
nw-buddy-project-reading

How the nWave buddy agent reads a project to answer questions — detection, order of inspection, and citation discipline.

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-buddy-project-reading
Stars
616
Forks
65
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 nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Buddy Project Reading 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/nWave-ai/nWave/tree/main/nWave/skills/nw-buddy-project-reading
  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/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-buddy-project-reading .claude/skills/nw-buddy-project-reading
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Buddy Project Reading 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 Nw Buddy Project Reading 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 Nw Buddy Project Reading 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 Reading for the Buddy Agent

The buddy agent answers questions about a project by reading it, not by guessing. Every answer must be traceable to a specific file and line. This skill is the reading protocol.

Goals

  1. Understand the project's architecture, current state, and methodology enough to answer the user's question.
  2. Cite every claim (path:line) so the user can verify.
  3. Avoid reading more than necessary — tokens and time both cost.
  4. Never invent file contents, function names, or architecture claims.

Order of inspection (cheap first)

Read in this order. Stop as soon as you have enough context for the current question.

1. Project identity (always, <1s)

  • README.md — what is this project, for whom.
  • CLAUDE.md (project root) — how Claude-based tools should behave here.
  • pyproject.toml / package.json / Cargo.toml / equivalent — language, dependencies, versions, scripts.
  • VERSION or the version field in the package manifest.

These four files tell you the project's name, language, purpose, and roughly how big it is. Skip nothing here.

2. Planning and state

  • BACKLOG.md — the single source of truth for planned work (in nWave projects).
  • CHANGELOG.md — recent user-visible changes.
  • git status — what's in flight right now.
  • git log --oneline -20 — recent direction of travel.

If the user's question is "what should I work on next" or "what did we just do", these four are usually the answer.

3. Architecture documentation

  • docs/architecture/architecture-design.md (or similar SSOT file).
  • docs/adrs/ — Architecture Decision Records.
  • docs/feature/ — feature specs with wave subdirectories.

The architecture doc is authoritative for design intent. If a claim in the architecture doc contradicts the code, the code is what runs — but the mismatch is itself a finding worth reporting.

4. Source layout

  • ls the top-level dirs.
  • ls src/ (or the project's source root).
  • Read the main entry point (main.py, index.ts, Main.fs, etc.).

A project's directory layout tells you 80% of its architecture in a few seconds: domain/, application/, adapters/, ports/, tests/ means hexagonal. src/, views/, controllers/, models/ means MVC. core/, api/, ui/ means layered.

5. Tests

  • tests/ or equivalent — top-level directories only, initially.
  • conftest.py / test config — tells you how tests are organized and marked.
  • A representative test file from each layer (unit, integration, acceptance, e2e).

Tests often document behavior more precisely than prose docs.

6. Specific files (on demand)

Once the question is clear, read the specific files it touches. Don't pre-load.

Detection heuristics

Answer these as you read:

  • Is this a nWave project? Clues: nWave/ directory, framework-catalog.yaml, BACKLOG.md at root, agents/commands/skills layout, mentions of waves in docs.
  • Is this greenfield or brownfield? Clues: git history length, number of commits, presence of legacy folders, CHANGELOG age.
  • What language(s)? pyproject.toml, package.json, .csproj, build.sbt, etc.
  • What architecture style? Directory names (see section 4 above).
  • What test strategy? Marker definitions, CI config, presence of acceptance/e2e folders.
  • What's the current branch? git status or git branch --show-current. Branch name often encodes the current focus.
  • Who owns what? CODEOWNERS if present.

Record the answers silently; draw on them when answering.

Wave progress detection

When reading a feature to answer "what's next?", check for these wave artifacts in order:

WaveArtifact PathExistence Check
DIVERGEdocs/feature/{id}/diverge/recommendation.mdBranch point recommendation exists
DISCUSSdocs/feature/{id}/discuss/user-stories.mdUser stories written
DESIGNdocs/feature/{id}/design/wave-decisions.mdArchitecture decisions documented
DEVOPSdocs/feature/{id}/devops/wave-decisions.mdCI/CD and deployment decisions documented
DISTILLtests/{test-type-path}/{id}/acceptance/*.featureBDD test scenarios written (the .feature file is the SSOT)
DELIVERdocs/feature/{id}/deliver/roadmap.jsonAll implementation steps at COMMIT/PASS

Stop at the first missing artifact — that's where the feature currently is. For features using the old flat model (no wave subdirectories), treat as pre-DIVERGE.

Citation discipline

Every concrete claim in an answer must point to a source:

  • "The TDD cycle has 5 phases (src/des/data/workflows/tdd-deliver.yaml:12)"
  • "Hexagonal boundaries defined in docs/architecture/architecture-design.md section 3"
  • "Current branch is feature/foo (from git status)"

If you're paraphrasing, still cite. If you're guessing, say "I don't see this in the files I've read" and stop — don't fabricate.

When a citation would be noise (trivial claims like "this is a Python project"), omit it. When a claim is load-bearing, always cite.

What NOT to do

  • Don't read the whole repo. It's expensive and usually unnecessary.
  • Don't read generated files. node_modules/, .venv/, build/, dist/, target/, lock files (unless the question is about deps).
  • Don't read test output or logs as primary sources. They're stale by the time you see them.
  • Don't invent file contents when a grep returns nothing. Report "not found" and suggest where it might live.
  • Don't answer without reading. If you can't ground the answer in the repo, say so.

Escalation

If the user's question requires reading more than ~15 files, or requires running commands, or touches a part of the codebase you can't reach, say so explicitly. Offer a plan: "to answer this I would need to read X, Y, Z — proceed?"

Example: "what is this project and where are we with it?"

A well-formed answer:

  1. Read README.md, pyproject.toml, CLAUDE.md — get the elevator pitch and version.
  2. Read BACKLOG.md — get the current priorities.
  3. Run git status + git log --oneline -10 — get the current branch and recent direction.
  4. Synthesize: "This is <name> v<version>, a <one-sentence purpose> written in <language>. The current branch is <branch>. Recent commits focus on <summary>. The backlog's top items are <top 3>. (citations for each claim)"

Total reading: 4-5 files, under a minute. That's the standard to aim for.

Frequently asked questions

What does the Nw Buddy Project Reading AI skill do?

How the nWave buddy agent reads a project to answer questions — detection, order of inspection, and citation discipline.

Why use Nw Buddy Project Reading on TypingMind?

Because you install it once and use it with any model. Nw Buddy Project Reading 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 Nw Buddy Project Reading in TypingMind?

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

Which AI models can use Nw Buddy Project Reading?

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 Nw Buddy Project Reading?

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

Is the Nw Buddy Project Reading AI skill free?

Yes. It is published on GitHub by nWave-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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