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 commands/lee-w/maigo/take-issuegit clone --depth 1 https://github.com/Lee-W/maigoWhat 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 | $0.00086 | $0.01385 |
| Opus 5 | $0.00043 | $0.00692 |
| Sonnet 5 | $0.00017 | $0.00277 |
| Haiku 4.5 | $0.00009 | $0.00138 |
Grade A, and why
take-issue scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/maigo:take-issue
接住 /maigo:triage-issue
判定 READY 之後斷掉的那一段——把 issue 接進真正的實作。
使用
/maigo:take-issue <issue 編號或 URL>
流程
1. 前置抓料（orchestrator 親跑，不開新 agent）
gh issue view <n> --json title,body,labels,comments
若曾跑過 /maigo:triage-issue，.maigo/ 底下可能有這條 issue 的 triage 產物：呼叫
python3 "${CLAUDE_PLUGIN_ROOT:-.}/scripts/artifact_path.py" triage-rubric --url <issue url> --topic "Triage rubric: <title> (#<n>)"
取得這條 issue 的候選路徑，path: 那行指的檔案存在就讀；status: new（新路徑還沒被寫過）
或 status: conflict（識別碼被別的主題占用）→ 退回讀舊 .maigo/triage-rubric.md（legacy_exists:
那行，只可讀）。兩者都讀不到也不擋，就當沒有先前 triage 產物繼續。把 issue
body + comments 整理成需求敘述：acceptance criteria 從 body 與 maintainer 在 comments 的
補充萃取，帶著這份 issue context 進下一步。
邊界：issue 明顯不是 READY 形狀（缺重現步驟、需求空泛、單純提問）→ 停下建議先跑
/maigo:triage-issue，不硬做。
2. Teammate flow
依 skills/teammate-flow
走完整流程——🐱 樂奈探索（帶著步驟 1 的 issue context，「看完了。相關的在這三個檔案。」）→
🩵 燈寫 plan（必須引用 issue 編號與萃取出的 acceptance criteria，「……讓我先理清楚它想
做什麼。」）→ 使用者確認 → 🎀 愛音實作 → 🟡 爽世完整 9 項 review → 🟣 立希驗證。流程細節
不在此重抄。
3. 收尾
🟣 立希全綠後，依 skills/git-workflow /
skills/commit-message
草擬 commit（body 帶 issue 參照，如 Fixes #<n> 或 repo 既有慣例）——不自動 commit、不
push、不開 PR。完成後提示可接 /maigo:describe-pr 產 PR title/description。
4. Work Board 回寫
依 skills/work-board 的 upsert 合約
更新 .maigo/board.md：
- 開工時：🐛 issue 行標
IN_PROGRESS，旁註 branch 名，留在 🎯 下一件（rank P5——半成品排在 「卡住的／球被打回／一步就結束／等你審」之後，因為它沒卡住任何人） - 收尾若已開 PR 或使用者提供 PR 編號：新增 / 更新 🔀 你的 PR 行到 ⏳ 等別人
等 review，issue 行旁註 linked PR
回寫時必須保留原 checkbox 與 🧠 標記；maigo 自己處理的項目不自動勾 checkbox。
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.
- 3d ago First seen · 78 lines · 86 tokens per session scan A 8b2651b3aff0
take-issue is a command published in the GitHub repository Lee-W/maigo (14 stars, last pushed 9d ago), licensed MIT. It adds 86 tokens to every session and 1,385 once invoked, about $0.0004 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 2026-08-30.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.