본문으로 건너뛰기
cccskills
무료GitHub에 공개

executing-plans

Use when you have a written implementation plan to execute inline in the current session with review checkpoints. Prefer subagent-driven-development when sub-agent dispatch is available.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.0 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Executing Plans

Overview

Load plan, review critically, execute all tasks, report when complete.

Announce at start: "I'm using the executing-plans skill to implement this plan."

Note: This skill drives task-by-task execution inline in the current session. If your platform supports sub-agent dispatch (delegate_task, spawn_worker), the subagent-driven-development skill produces higher-quality results with two-stage review and isolated contexts. Use this skill when sub-agent dispatch is not available or your human partner explicitly chose inline execution.

The Process

Step 1: Load and Review Plan

  1. Read the plan file with read_file.
  2. Review critically — identify any questions or concerns about the plan.
  3. If concerns: raise them with your human partner before starting.
  4. If no concerns: create a todo per task and proceed.

Step 2: Execute Tasks

For each task:

  1. Mark the todo as in_progress.
  2. Follow each step exactly (the plan has bite-sized steps).
  3. Run verifications as specified — don't skip them.
  4. Mark the todo as completed only when the step's verification passes.

Step 3: Complete Development

After all tasks complete and verified:

  • Announce: "All plan tasks complete. Tests pass. Ready to wrap up."
  • Run any final-stage verification the plan demands (full test suite, lint, type checks).
  • Ask your human partner how they want to integrate the work (merge, PR, hand off).
  • Do not push, merge, or open a PR without explicit confirmation.

When to Stop and Ask for Help

STOP executing immediately when:

  • You hit a blocker (missing dependency, test fails, instruction unclear).
  • The plan has critical gaps preventing the next step.
  • You don't understand an instruction.
  • A verification fails repeatedly after the same fix attempt.

Ask for clarification rather than guessing.

When to Revisit Earlier Steps

Return to Review (Step 1) when:

  • Your human partner updates the plan based on your feedback.
  • The fundamental approach needs rethinking.

Don't force through blockers — stop and ask.

Remember

  • Review plan critically first.
  • Follow plan steps exactly.
  • Don't skip verifications.
  • Reference skills when the plan says to (use skill_view).
  • Stop when blocked — don't guess.
  • Never start implementation on the main/master branch without explicit consent.

Integration

Related skills:

  • writing-plans — Creates the plan this skill executes.
  • subagent-driven-development — Higher-quality alternative when sub-agent dispatch is available.
  • systematic-debugging — Use when a verification fails and the cause isn't obvious.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

The Arbor research-coordinator protocol: the OBSERVE -> IDEATE -> SELECT -> DISPATCH -> DECIDE cycle over a durable Idea Tree. Preloaded automatically on /auto-research sessions. The coordinator never edits code or runs commands — executors do that in isolated worktrees; the coordinator steers the tree with idea_tree / dispatch_experiments / merge_experiment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

invergent-ai/surogates282026년 10월 20일 업데이트

The Arbor executor workflow: clone the repo from the git bundle your brief hands you, implement and evaluate exactly ONE hypothesis with the file tools, then report structured results via worker_complete. Preloaded automatically on arbor-executor task workers. Never touch the held-out test split.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

invergent-ai/surogates282026년 10월 20일 업데이트

Hard-gated ideation for an Arbor research run. Load at the START of every IDEATE round, before drafting any hypothesis. Enforces the PI mindset (mechanism over knob), the four-question first-principles probe, the kill-filter, and the four-line hypothesis format. Ported from Arbor's idea_drafting + first_principles_probe.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

invergent-ai/surogates282026년 10월 20일 업데이트

DECIDE-phase doctrine for an Arbor research run: when to merge, prune, combine, or finalize; how the held-out merge gate works; and the rule that the final report uses TEST scores. Load before deciding what to do with completed experiments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

invergent-ai/surogates282026년 10월 20일 업데이트

Intake for an autonomous research run (Arbor). Use when the user wants to optimize a metric in a repo over many isolated experiments — 'optimize this benchmark', 'improve the model F1 overnight', 'beat the leaderboard'. Discovers the repo/eval/splits, measures the baseline, confirms a Research Contract, then emits a ready-to-send /auto-research command. Does NOT start the run itself.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

invergent-ai/surogates282026년 10월 20일 업데이트

You MUST use this before any creative work — creating features, building components, drafting documents, designing workflows, or modifying behavior. Explores your human partner's intent, requirements, and design before any implementation begins.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

invergent-ai/surogates282026년 10월 20일 업데이트

invergent-ai의 스킬 모두 보기

이 스킬의 문제 신고