> 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.
npx skills add https://github.com/atlassian/twg-cli --skill twg-context-discovery
Use the root twg skill. Get command grammar from live twg help,
twg help <terms>, or twg help describe <path>.
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.
Resolve the anchor before widening:
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.
context.
meeting evidence.
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.
../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.
evidence. Map broad subdomains before assigning owners/experts.
Use them only when the user explicitly asks for that query language or typed
commands cannot express the required edge.
For central candidates, use a bounded source and relationship fan-out:
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.
design, PR, commit, branch, assignee, reporter, contributor, and reviewer
signals when they change direction, risk, ownership, or next action.
the whole graph blindly.
evidence against the requested output. If owner, status, relation, recency,
and evidence URL/key are present, synthesize instead of widening.
twice, do not keep probing adjacent graph paths. Record the coverage gap and
continue with product-native hydrated evidence.
teams, decisions, ownership, risk, or next action.
For graph requests, pipe typed context output to twg visualize. Keep entities
that change direction, ownership, risk, or next action; collapse duplicates.
add a relationship table. For other context work, include entity,
relationship, owner, importance, and evidence.
sampled.
stdout_shape as a complete entity or URL inventory."Done".
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
Take atlassian/twg-context-discovery from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.