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/johardi/claude-stack/compose-ticketsnpx skills add johardi/claude-stack --skill compose-ticketsgit clone --depth 1 https://github.com/johardi/claude-stackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/johardi/claude-stack/compose-tickets)<a href="https://agentmods.dev/skills/johardi/claude-stack/compose-tickets"><img src="https://agentmods.dev/badge/skills/johardi/claude-stack/compose-tickets.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00118 | $0.02359 |
| Opus 5 | $0.00059 | $0.01179 |
| Sonnet 5 | $0.00024 | $0.00472 |
| Haiku 4.5 | $0.00012 | $0.00236 |
Grade A, and why
compose-tickets 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 4d 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compose Tickets
Walk a planning conversation toward a structured parent GitHub issue plus linked sub-tickets, in a format that the orchestrate-tickets skill can execute directly. Companion to orchestrate-tickets: this skill composes the score, that skill performs it. Two confirmation gates: one before drafting bodies, one before filing.
When to Use
Use when:
- The user is describing a new feature, refactor, or initiative and the discussion reveals it touches multiple modules or has natural sequencing.
- The work would produce a PR too large to review as one unit.
- The user explicitly asks to "break this down", "split into tickets", "draft sub-tickets", or "open a parent issue with children".
Skip when:
- The work is a single small PR — recommend the
github-issue-workflowskill and stop. - The user wants to implement immediately without filing tickets — also
github-issue-workflow.
Prerequisites
gh auth status— GitHub CLI authenticated.git rev-parse --git-dir— inside a git repository (sogh issuetargets the right repo).
If either check fails, stop and surface the error. Do not auto-fix authentication.
Workflow
Phase 1 — Understand the feature
Listen to the user's description. Ask only the clarifying questions that materially change scope or decomposition. Avoid 20-questions interrogation. Use AskUserQuestion only when a missing detail would change the slicing.
Examples of questions that justify asking:
- "Is this a one-off migration or a recurring capability?"
- "Should X be configurable per project, or global?"
- "Does this need a database migration?"
Examples of questions that do NOT justify asking (decide silently or surface as assumption):
- styling preferences
- exact field names
- naming of internal helpers
Phase 2 — Optional codebase exploration
If the description names files, modules, or systems, optionally spawn one Agent of subtype Explore (thoroughness: "medium") to:
- Confirm which modules/files are actually touched.
- Surface existing functions and utilities that should be reused (avoid proposing duplicate code).
- Identify any data-model or schema dependencies between proposed slices.
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.
- 4d ago First seen · 219 lines · 118 tokens per session scan A 0837157eaa0c
compose-tickets is a skill published in the GitHub repository johardi/claude-stack (2 stars, last pushed 4mo ago), licensed MIT. It adds 118 tokens to every session and 2,359 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.
auto-perf-optimize
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…