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resolve-todo-parallel
Resolve all pending CLI todos using parallel processing, compound on lessons learned, then clean up completed todos.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Resolve all pending CLI todos using parallel processing, compound on lessons learned, then clean up completed todos.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
Run comprehensive agent-native architecture review with scored principles
Explore requirements and approaches through collaborative dialogue before writing a right-sized requirements document and planning implementation. Use for feature ideas, problem framing, when the user says 'let's brainstorm', or when they want to think through options before deciding what to build. Also use when a user describes a vague or ambitious feature request, asks 'what should we build', 'help me think through X', presents a problem with multiple valid solutions, or seems unsure about scope or direction — even if they don't explicitly ask to brainstorm.
Refresh stale or drifting learnings and pattern docs in docs/solutions/ by reviewing, updating, replacing, or archiving them against the current codebase. Use after refactors, migrations, dependency upgrades, or when a retrieved learning feels outdated or wrong. Also use when reviewing docs/solutions/ for accuracy, when a recently solved problem contradicts an existing learning, or when pattern docs no longer reflect current code.
Document a recently solved problem to compound your team's knowledge
Generate and critically evaluate grounded improvement ideas for the current project. Use when asking what to improve, requesting idea generation, exploring surprising improvements, or wanting the AI to proactively suggest strong project directions before brainstorming one in depth. Triggers on phrases like 'what should I improve', 'give me ideas', 'ideate on this project', 'surprise me with improvements', 'what would you change', or any request for AI-generated project improvement suggestions rather than refining the user's own idea.
| name | resolve-todo-parallel |
| description | Resolve all pending CLI todos using parallel processing, compound on lessons learned, then clean up completed todos. |
| argument-hint | [optional: specific todo ID or pattern] |
Resolve all TODO comments using parallel processing, document lessons learned, then clean up completed todos.
Get all unresolved TODOs from .context/compound-engineering/todos/*.md and legacy todos/*.md
Residual actionable work may come from ce:review-beta mode:autonomous after its in-skill safe_auto pass. Treat those todos as normal unresolved work items; the review skill has already decided they should not be auto-fixed inline.
If any todo recommends deleting, removing, or gitignoring files in docs/brainstorms/, docs/plans/, or docs/solutions/, skip it and mark it as wont_fix. These are compound-engineering pipeline artifacts that are intentional and permanent.
Create a task list of all unresolved items grouped by type (e.g., TaskCreate in Claude Code, update_plan in Codex). Analyze dependencies and prioritize items that others depend on. For example, if a rename is needed, it must complete before dependent items. Output a mermaid flow diagram showing execution order — what can run in parallel, and what must run first.
Spawn a compound-engineering:workflow:pr-comment-resolver agent for each unresolved item.
If there are 3 items, spawn 3 agents — one per item. Prefer running all agents in parallel; if the platform does not support parallel dispatch, run them sequentially respecting the dependency order from step 2.
Keep parent-context pressure bounded:
If the todo set is large enough that even batched short returns are likely to get noisy, use a per-run scratch directory such as .context/compound-engineering/resolve-todo-parallel/<run-id>/:
GATE: STOP. Verify that todos have been resolved and changes committed. Do NOT proceed to step 5 if no todos were resolved.
Load the ce:compound skill to document what was learned from resolving the todos.
The todo resolutions often surface patterns, recurring issues, or architectural insights worth capturing. This step ensures that knowledge compounds rather than being lost.
GATE: STOP. Verify that the compound skill produced a solution document in docs/solutions/. If no document was created (user declined or no non-trivial learnings), continue to step 6.
Search both .context/compound-engineering/todos/ and legacy todos/ for files with done, resolved, or complete status, then delete them to keep the todo list clean and actionable.
If a per-run scratch directory was created at .context/compound-engineering/resolve-todo-parallel/<run-id>/, and the user did not ask to inspect it, delete that specific <run-id>/ directory after todo cleanup succeeds. Do not delete any other .context/ subdirectories.
After cleanup, output a summary:
Todos resolved: [count]
Lessons documented: [path to solution doc, or "skipped"]
Todos cleaned up: [count deleted]