Twg Context Discovery logo

Twg Context Discovery

Organization
zenobi-us
twg-context-discovery

Use with root `twg` for deep context, dependency maps, related entities, project-to-repo discovery, OOO catch-ups, and "catch me up" requests around a concrete anchor.

Overview

Publisherzenobi-us
Repositorydotfiles
Skill nametwg-context-discovery
Stars
67
Forks
6
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 zenobi-us on GitHub. Read the source before you install it.

Installation

Install the Twg Context 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.
  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/zenobi-us/dotfiles.git /tmp/dotfiles
mkdir -p .claude/skills
cp -r /tmp/dotfiles/files/devtools/agent/bundles/developer/skills/atlassian/twg-context-discovery .claude/skills/twg-context-discovery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Twg Context 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 Twg Context 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 Twg Context 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.

twg-context-discovery

Use the root twg skill. Get command grammar from live twg help, twg help <terms>, or twg help describe <path>.

CLI launcher fallback

Run twg <command>. On shell command not found, use $HOME/.local/bin/twg (macOS/Linux) / $env:LOCALAPPDATA\Programs\twg\bin\twg.exe (PowerShell), then tell user to add that directory to PATH. Do not treat auth or command errors as PATH failures.

First Move

Resolve the anchor before widening:

  • Stable key, URL, or ARI: use directly when the family is clear.
  • Fuzzy topic or name: classify scope; hydrate 2-5 anchors before ranking.
  • Multiple same-kind anchors: batch them in one context call when supported.
  • Unknown command shape: inspect focused help before calling data.

For fuzzy topics, group high-signal candidates by scope using explicit charter, roadmap, project, product, or service evidence. Keep same-named feature, platform, domain, team, and initiative clusters separate. Compare scope fit, centrality, breadth, and recency before selecting one. If ambiguity remains, show alternatives or ask. Set the boundary before inferring experts or ownership; nearby authorship or activity does not prove broader responsibility.

If context is not advertised for an anchor type, use product-native hydration and search evidence instead of inventing paths.

For ownership, expertise, approval authority, leadership reach-outs, or escalation, load ../twg-responsibility-routing/SKILL.md. Return here only when that workflow needs relationship or dependency expansion.

Route Selection

  • Known Jira work items usually need native workitem details plus relationship context.
  • Projects and goals need native details plus Jira, docs, search, PR, and meeting evidence.
  • For topic onboarding, search knowledge and product-native work once. Compare formal epic, project, goal, and page anchors across same-named scopes; source-defined hierarchy distinguishes the central program/platform from a feature, migration, or adoption effort. Prefer the anchor linking current delivery work and code. Hydrate it, then use context and responsibility once each only if they add dependencies or people. Hydrate at most three items. Never refetch a source with another projection or try more synonyms after resolution. Target 6-10 calls; stop once the categories are supported.
  • For restart, handoff, or OOO catch-up, load ../twg-status-rollups/references/personal-work-summary.md and follow its restart guidance. Infer priority across connected evidence and hydrate only anchors that change the user's next action.
  • Dependency map and page/topic prompts need hydrated anchors before broad search is evidence. Map broad subdomains before assigning owners/experts.
  • Raw graph-query/debugging surfaces are not the default dependency-map route. Use them only when the user explicitly asks for that query language or typed commands cannot express the required edge.

Evidence Policy

For central candidates, use a bounded source and relationship fan-out:

  • Source fetch: fields, owner, status, body, comments, and URLs.
  • Context: graph edges, formal external links, related people, teams, projects, goals, docs, PRs, commits, and branches.

Use summary detail first. Escalate to full only for the central anchor or up to 3 high-signal related anchors when URLs, comments, body content, or provenance are missing.

Treat third-party URLs as graph nodes. Collect remote links, context edges, descriptions, comments, ADF links, bare URLs, and linked bodies; retain provenance for relationship direction.

Expansion Rules

  • Expand by relationship role, not raw count.
  • Hydrate parent, epic, inbound peer, blocker, consumer, central page, external design, PR, commit, branch, assignee, reporter, contributor, and reviewer signals when they change direction, risk, ownership, or next action.
  • Fetch known older links directly by URL, key, ID, or ARI instead of widening the whole graph blindly.
  • Use strong query variants rather than many synonyms.
  • After the first source fetch plus context/search pass, pause and compare the evidence against the requested output. If owner, status, relation, recency, and evidence URL/key are present, synthesize instead of widening.
  • If a context or graph-backed command returns the same backend/coverage error twice, do not keep probing adjacent graph paths. Record the coverage gap and continue with product-native hydrated evidence.
  • Stop when the next candidate would not add new entities, links, contributors, teams, decisions, ownership, risk, or next action.

Graph Visualization

For graph requests, pipe typed context output to twg visualize. Keep entities that change direction, ownership, risk, or next action; collapse duplicates.

Output Shape

  • Anchor snapshot: what it is and why it matters.
  • For OOO catch-ups, synthesize by priority workstream and next action; do not add a relationship table. For other context work, include entity, relationship, owner, importance, and evidence.
  • Risks and dependencies, separating confirmed edges from inferred relationships.
  • Suggested next actions.
  • Confidence and gaps when evidence is incomplete, access-limited, stale, or sampled.

Anti-Patterns

  • Do not stop at search results without hydrating anchors.
  • Do not treat stdout_shape as a complete entity or URL inventory.
  • Do not skip peer expansion for graph/dependency prompts because peers look "Done".
  • Do not dismiss a 1-hop candidate by title alone.
  • Do not hand-roll graph HTML.

Frequently asked questions

What does the Twg Context Discovery AI skill do?

Use with root `twg` for deep context, dependency maps, related entities, project-to-repo discovery, OOO catch-ups, and "catch me up" requests around a concrete anchor.

Why use Twg Context Discovery on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zenobi-us/dotfiles/tree/master/files/devtools/agent/bundles/developer/skills/atlassian/twg-context-discovery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Twg Context 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 Twg Context Discovery?

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

Is the Twg Context Discovery AI skill free?

Yes. It is published on GitHub by zenobi-us 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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