Twg Agentic Search logo

Twg Agentic Search

Organization
zenobi-us
twg-agentic-search

Use with root `twg` for deep iterative enterprise/company knowledge search and internal research with Rovo Search across connected apps/connectors including Confluence, Jira, Drive, Slack, Bitbucket, and GitHub.

Overview

Publisherzenobi-us
Repositorydotfiles
Skill nametwg-agentic-search
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 Agentic Search 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-agentic-search .claude/skills/twg-agentic-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Twg Agentic Search 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 Agentic Search 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 Agentic Search 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-agentic-search

Use together with the root twg skill. Exact command grammar comes from live twg help, especially twg help describe "rovo search" when filter or output options matter.

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.

Workflow

  1. Classify the request as fuzzy or cross-product internal research. Prefer this skill when the source is unclear, current company knowledge is needed, or the answer may span Confluence, Jira, Drive, Slack, Bitbucket, GitHub, or other Rovo-connected apps.
  2. Confirm or infer the Atlassian site. Ask only when no configured or explicit site is available and the ambiguity would change the search.
  3. If app/source availability changes the plan, run twg rovo list-apps -o json or twg search list-apps -o json. Use the returned built-ins, connectors, readiness, and auth/setup actions to decide scope; do not start setup or login unless the user asked for it.
  4. Start with one query that combines the concrete topic with the requested artifact or decision type. Use at most one canonical/authoritative or recent/update-oriented refinement when the first result set mixes scopes, lacks primary sources, or misses the requested time signal. Do not fan out exact-title searches for every candidate already returned by the primary query.
  5. Choose filters deliberately. Default to Confluence and Jira built-ins for official/internal knowledge. Broaden to Slack, Google Drive, Bitbucket, GitHub, or other connectors only when useful and available. Use app, type, recency, owner/contributor/assignee/reporter/status, title-only, label/space, and site filters when they narrow evidence without hiding likely answers.
  6. Search with bounded output:
bash
twg rovo search "<query>" --output json --output-summary auto --agent-fields @compact
twg rovo search "<query>" --output json --output-summary auto --agent-fields @evidence

Use @compact to shortlist candidates and @evidence when snippets, URLs, and provenance need more detail.

Evidence Rules

  • Treat search snippets as candidates, not facts.
  • Hydrate a small, diverse primary-source set before final claims. For document discovery, cover distinct roles such as requirements, architecture/design, and current delivery rather than redundant pages. Use product-native commands such as twg confluence content get, twg jira workitem get, twg jira workitem query, twg bb prs, twg bb repo, or the relevant product command for the result URL/type.
  • For document or PRD discovery, select at most five sources across those roles. Hydrate one source per role unless a material conflict requires a second. Stop once the roles, current delivery, and important conflicts are supported, even when search returns more candidates.
  • Prefer official spaces, owned project pages, current Jira issues, and recent decision records over personal drafts or stale chat mentions, unless the user explicitly asked for informal signal.
  • Compare hydrated evidence for conflicts, recency, ownership, and authority.
  • Do not rerun an @evidence search for a candidate that can be hydrated through its product-native URL or ID, and do not refetch one source under another projection. Call out ACL gaps, unavailable connectors, low recall, and unresolved contradictions instead of flattening them into a single claim.

Output

Lead with the answer or best-supported conclusion. Cite hydrated titles/URLs and include the source app, date or status when available, and why each source was trusted. Separate confirmed facts, likely interpretations, conflicts, and gaps.

Frequently asked questions

What does the Twg Agentic Search AI skill do?

Use with root `twg` for deep iterative enterprise/company knowledge search and internal research with Rovo Search across connected apps/connectors including Confluence, Jira, Drive, Slack, Bitbucket, and GitHub.

Why use Twg Agentic Search on TypingMind?

Because you install it once and use it with any model. Twg Agentic Search 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 Agentic Search 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-agentic-search. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Twg Agentic Search?

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 Agentic Search?

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

Is the Twg Agentic Search 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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