Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/juxt/claude-plugins/goal-treenpx skills add juxt/claude-plugins --skill goal-treegit clone --depth 1 https://github.com/juxt/claude-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00110 | $0.01135 |
| Opus 5 | $0.00055 | $0.00567 |
| Sonnet 5 | $0.00022 | $0.00227 |
| Haiku 4.5 | $0.00011 | $0.00113 |
Grade A, and why
goal-tree scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chalk Goal Trees
Interpret MUST, MUST NOT, SHOULD, SHOULD NOT, MAY, etc. per RFC 2119.
A goal tree is a mindmap whose relation is serves, not supports.
Load chalk:mindmap first — subject lines, bolding, shallow nesting, tags and typed IDs all apply here unchanged.
The shape
The root is the goal. Each node's children are what it takes to achieve it, recursively, down to leaves that are directly actionable.
Every node's children MUST be work that accomplishes it, not evidence that argues for it.
If they argue rather than accomplish, it's an argument tree; see chalk:mindmap.
The test at each node is sufficiency
Assume every child is done, then ask whether the parent is thereby achieved.
Not "do these look related to the parent?" but "do these, plus what we already know about this system, get us there?"
-
The domain knowledge is part of the test. Say that out loud rather than leaning on it silently.
-
Each node MUST be tested for sufficiency explicitly rather than assumed, and any node whose children are not clearly sufficient MUST be marked
check:rather than left to read as settled. -
This is why goal trees don't need the decomposition note an argument tree needs (see
chalk:mindmap).
To find a missing child, ask what would stop the parent rather than what would achieve it.
Every leaf is one of three things — say which
A leaf MUST declare which kind it is wherever that isn't obvious.
- Something we do.
- Something expected of someone or something else The user, CI, another team, an upstream library, existing behaviour.
- A plain fact about the world we're relying on.
An expectation of someone else MUST NOT be written as though it were our own task. It reads as covered, it sits in the tree looking like work, and nothing happens until someone notices it was never assigned.
assumption: covers whether a leaf has been verified; this is the separate question of who's on the hook.
A leaf can be both — a verified fact about an upstream library is still someone else's to keep true.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 89 lines · 110 tokens per session scan A 57c3f80912bb
goal-tree is a skill published in the GitHub repository juxt/claude-plugins (10 stars, last pushed 4d ago), licensed MIT. It adds 110 tokens to every session and 1,135 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.