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Nw Buddy Ssot Knowledge

Organization
nWave-ai
nw-buddy-ssot-knowledge

Single Source of Truth detection — where truth lives in an nWave repo and how to avoid contradicting it.

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-buddy-ssot-knowledge
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 Ssot Knowledge 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-ssot-knowledge
  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-ssot-knowledge .claude/skills/nw-buddy-ssot-knowledge
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Buddy Ssot Knowledge 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 Ssot Knowledge 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 Ssot Knowledge 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.

SSOT Knowledge for the Buddy Agent

Large projects accumulate multiple copies of "the same" information: a version in pyproject.toml, a version in a VERSION file, a version in the README, a version in a changelog. When these disagree, the buddy agent must know which one is authoritative. This skill is the SSOT map for nWave projects.

Why it matters

Answering a user's question with a stale secondary copy is worse than saying "I don't know": it spreads wrong information with a confident tone. The buddy's job is to read the authoritative source every time, and to flag contradictions when the copies diverge.

The SSOT map

Each concept below has one authoritative file. Read that file first. Treat everything else as a cache that may be stale.

Versions

  • Authoritative: pyproject.toml [project] version = "..." (or the equivalent field in the package manifest for non-Python projects).
  • Caches: VERSION file, README badges, docs footer, release notes.
  • Rule: quote pyproject.toml. If asked about a cache that disagrees, report the discrepancy.

Planned work

  • Authoritative: BACKLOG.md at the repo root.
  • Caches: GitHub issues, conversation context, notes in other docs, stale TODO comments.
  • Rule: work that isn't in BACKLOG.md is not planned. Don't invent items. If the user mentions something not on the backlog, point that out and suggest they add it.

Architecture intent

  • Authoritative: docs/architecture/architecture-design.md (or whichever file the project uses as the single architecture doc — usually linked from CLAUDE.md or README.md).
  • Caches: individual feature specs in docs/feature/ with wave subdirectories, ADRs in docs/adrs/, diagrams in docs/, comments in code.
  • Rule: design intent lives in the architecture doc; design decisions live in ADRs; design details live in feature specs. If they disagree, the architecture doc wins for high-level questions; ADRs win for "why did we choose X".

Runtime behavior

  • Authoritative: the code.
  • Caches: docs, specs, comments, commit messages.
  • Rule: if a doc claims behavior X and the code does Y, the code is what users experience. Report the discrepancy as a finding.

Configuration

  • Authoritative: the config file the runtime actually loads (pyproject.toml, .pre-commit-config.yaml, ci.yml, whichever applies).
  • Caches: docs describing the config, README examples.
  • Rule: quote the real file, including its path.

Agent / command definitions

  • Authoritative: the YAML frontmatter of the agent or command file, and the framework catalog (framework-catalog.yaml or equivalent) if the project uses one.
  • Caches: READMEs, marketing docs, generated reference pages.
  • Rule: trust the frontmatter + catalog.

Git state

  • Authoritative: git status, git log, git branch.
  • Caches: conversation memory, "what I was doing" notes.
  • Rule: run git commands before answering state questions. Never guess.

Conflict resolution hierarchy

When two sources disagree and it isn't obvious which wins, use this hierarchy from most to least authoritative:

  1. The code itself (runtime behavior).
  2. The config files that control the runtime.
  3. The SSOT file declared by the project (BACKLOG.md, architecture-design.md, etc.).
  4. ADRs (for "why" questions).
  5. Feature specs.
  6. Generated docs.
  7. README.
  8. Prose in commit messages.
  9. Conversation context and memory.

Reporting a conflict is a valuable answer. "The README says X but the code does Y — you probably want to update the README" is more useful than picking one silently.

How to recognize a stale secondary copy

Signals that a doc is out of date:

  • It references APIs, commands, or files that no longer exist in the repo.
  • It claims a version number older than pyproject.toml.
  • Its "last updated" date (if present) is much older than recent commits to the code it describes.
  • Its vocabulary matches an older phase of the project (old feature names, old architecture terms).
  • It contradicts a more recent ADR.

When you spot one, mention it in the answer and suggest it be updated or deleted.

Read-before-answer discipline

Before any concrete answer:

  1. Identify which concept the question is about (version, backlog, architecture, behavior, config, state).
  2. Look up the SSOT for that concept in the map above.
  3. Read the authoritative file.
  4. Form the answer from that file, with a citation.
  5. If you also happen to see a contradicting cache, report it.

Don't answer from memory. The buddy agent's memory is not an SSOT; the repo is.

Never invent, always cite

If you don't find the information, say so and suggest where the user might add it. Never fabricate a file path, a function name, a version number, or a backlog item. Fabrication destroys trust; "I couldn't find this" preserves it.

A short example

User asks: "what version are we on and what's next?"

Good answer:

We're on v2.17.5 (pyproject.toml:3). The top three items in BACKLOG.md are:

  1. Finish the D-PLUGIN-00b delivery (BACKLOG.md:12)
  2. Docker validation matrix refresh (BACKLOG.md:27)
  3. CI matrix expansion (BACKLOG.md:34)

Note: the README badge shows v2.17.3 — probably stale, consider updating.

Bad answer:

I think we're on v2.17 something and you're working on the plugin feature.

The good answer is sourced, cited, and adds a finding. The bad answer is vibes.

Frequently asked questions

What does the Nw Buddy Ssot Knowledge AI skill do?

Single Source of Truth detection — where truth lives in an nWave repo and how to avoid contradicting it.

Why use Nw Buddy Ssot Knowledge on TypingMind?

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

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

Which AI models can use Nw Buddy Ssot Knowledge?

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 Ssot Knowledge?

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

Is the Nw Buddy Ssot Knowledge 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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