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Executing Plans

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GanyuanRan
executing-plans

Use when executing a written implementation plan across sessions or with review checkpoints. Small or single-slice plans stay inline. For same-session independent tasks, use subagent-driven-development instead.

Overview

PublisherGanyuanRan
RepositoryAegis
Skill nameexecuting-plans
Stars
1.2K
Forks
52
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by GanyuanRan on GitHub. Read the source before you install it.

Installation

Install the Executing Plans AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GanyuanRan/Aegis.git /tmp/Aegis
mkdir -p .claude/skills
cp -r /tmp/Aegis/skills/executing-plans .claude/skills/executing-plans
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Executing Plans in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Executing Plans on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Executing Plans is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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."

For non-trivial execution, include Aegis Visibility: briefly tie the active slice to its plan, checkpoint, drift or verification boundary. At completion, pass plan adherence, evidence, complexity and residual risk to verification-before-completion for the unified receipt.

If subagents are available and the plan has genuinely independent tasks, prefer subagent-driven-development; lack of subagent support does not block inline execution. Same-task agents share one workspace, and the coordinator remains the only Git mutation owner.

The Process

Step 1: Load and Review Plan

  1. Read plan file
  2. If the plan or active checkpoint includes an Execution Readiness View, read it before implementation and compare the plan against its intent lock, scope fence, baseline lock, owner / contract constraints, compatibility boundary, retirement boundary, test obligations, review gates, drift / rewind rules, and evidence required before completion.
  3. Review critically - identify any questions or concerns about the plan
  4. If the view contradicts the plan, baseline, or current worktree evidence, return to plan review or refresh the advisory handoff before editing.
  5. Run the TDD Route Guard before implementation: confirm Mode, Decision, Strict authority, strict signals, light eligibility, Test posture, and verification. Strict steps require either an explicit user/project request or a recorded auto decision; plan approval or risk labels alone are not authority. An off-mode missing record may be repaired only as Mode: off / Decision: skipped without loading TDD. Missing/unsupported auto decisions return to plan review. An auto-light record is unsupported when any strict signal is present or when its tiny/low-risk/single-owner/no-behavior-change proof is incomplete. Only Decision: strict with recorded strict authority may authorize steps named Write failing test, Verify RED, GREEN, or REFACTOR. Do not infer strict during execution.
  6. If concerns: Raise them with your human partner before starting
  7. Before the first write, capture TaskStartSnapshot: root, HEAD, branch or detached state, upstream divergence, staged/unstaged/untracked paths, active Git operations, and git worktree list --porcelain. Preserve task-preexisting state; do not stash, reset, clean, or commit it.
  8. Reuse the current branch unless rules require independent history or another goal owns it. If justified, switch/create it in the current workspace when safe; a worktree still requires concurrent checkout or blocking dirty state.
  9. If no concerns: Create TodoWrite and proceed

Step 1.5: Long-Task Checkpoint Setup

If the plan has multiple tasks, may span sessions, or includes architecture / contract / workflow changes:

  1. Announce: "I'm using the long-task-continuation skill to keep this plan checkpointed and drift-aware."
  2. Load aegis:long-task-continuation.
  3. Create the initial checkpoint from the plan:
    • current todo
    • active task
    • completed tasks
    • evidence refs
    • blockers
    • next step
  4. Before each task, restate the current checkpoint.
  5. After each task, update checkpoint, evidence refs, and drift check.

Before a verification-driven unplanned edit, read retained PatchShape, CanonicalOwner, UpwardDrillSignal, outcome, and evidence refs. Route their comparison with the candidate to systematic-debugging before editing; it decides whether the directions converge. A proven independent canonical-owner root stays on the normal plan path.

Step 2: Execute Tasks

For each task:

  1. Mark as in_progress

  2. Follow each step exactly (plan has bite-sized steps)

  3. Before any new source-code path is added by a task, restate the plan's Change Necessity or create a compact one if the plan failed to carry it forward. Plan approval is not by itself proof that a new helper, small guard, new branch, fallback, adapter, or owner is necessary.

    text
    Change Necessity:
    - User-visible need:
    - No-change / non-code option:
    - Why code change is necessary:
    - Minimum change boundary:
    - Decision: no-change | docs/config-only | code-change | needs-clarification

    If the decision is not code-change, pause execution and return to plan review instead of editing. If the decision is code-change, carry the minimum boundary into the edit and verification scope.

  4. Before any non-trivial source edit, run the plan's Pre-Edit Complexity Check or create a compact one:

    Use using-aegis/references/complexity-governance.md for shared artifact classes, pressure signals, and over-budget handling.

    text
    Complexity Budget:
    - Artifact class:
    - Target files / artifacts:
    - Current pressure:
    - Projected post-change pressure:
    - Budget result: within-budget | at-risk | over-budget
    - Planned governance:
    
    Pre-Edit Complexity Check:
    - Target edit file:
    - Existing pressure signal:
    - Safer edit boundary:
    - Decision: edit-in-place | extract helper | add owner file | split task | pause for plan update
    
    Pre-Edit Owner-Fit Decision:
    - Edit intent: wiring-only | move-out / extract-first | local-fix-without-new-responsibility | new-responsibility | emergency / compatibility patch
    - Owner fit:
    - Safer edit boundary:
    - Decision: edit-in-place | extract helper | add owner file | split task | pause for plan update

    If the check contradicts the plan's file boundary, pause and return to plan review instead of silently stuffing logic into an overloaded owner. If the budget result is over-budget and the task does not also govern that overrun, stop execution and return to plan review rather than pushing the task through as if it were still atomic. When the target edit file is over-budget or mixed-purpose, new-responsibility must not be added in place by default. wiring-only, move-out / extract-first, and local-fix-without-new-responsibility may proceed only when they do not add a new responsibility and the verification boundary is clear. emergency / compatibility patch requires residual risk and a retirement trigger.

  5. Run verifications as specified

  6. The coordinator is the Git mutation owner. After the coherent Task passes its planned verification, use verification-before-completion before a default local commit, stage only task-owned paths, and read back HEAD, the committed file list, and remaining task delta. no commit, read-only, no-change, and failed-verification tasks create no normal commit.

  7. Update TodoCheckpointDraft and DriftCheckDraft before marking the task completed. When an Execution Readiness View exists, the drift check must explicitly compare the active slice against the view's intent lock, scope fence, baseline lock, compatibility boundary, retirement boundary, test obligations, and review gates.

  8. Mark as completed

Step 3: Complete Development

After all tasks complete and verified:

  • if Aegis created a branch/worktree or the user requests integration handling, use aegis:finishing-a-development-branch;
  • otherwise use verification-before-completion, report the local task commit and Task clean / Repository clean, and do not invent merge/PR ceremony.

When to Stop and Ask for Help

STOP executing immediately when:

  • Hit a blocker (missing dependency, test fails, instruction unclear)
  • Plan has critical gaps preventing starting
  • You don't understand an instruction
  • Verification fails repeatedly

Ask for clarification rather than guessing.

When to Revisit Earlier Steps

Return to Review (Step 1) when:

  • Partner updates the plan based on your feedback
  • 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 plan says to
  • Stop when blocked, don't guess
  • Do not create a branch merely because the current branch is main/master
  • Do not let task complexity, TDD, planning, or subagents alone trigger a worktree

Integration

Required workflow skills:

  • aegis:writing-plans - Creates the plan this skill executes
  • aegis:using-git-worktrees - Only when the approved Git lifecycle says a concurrent checkout is necessary
  • aegis:finishing-a-development-branch - Only when branch/worktree integration or cleanup is in scope

Frequently asked questions

What does the Executing Plans AI skill do?

Use when executing a written implementation plan across sessions or with review checkpoints. Small or single-slice plans stay inline. For same-session independent tasks, use subagent-driven-development instead.

Why use Executing Plans on TypingMind?

Because you install it once and use it with any model. Executing Plans is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Executing Plans in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GanyuanRan/Aegis/tree/main/skills/executing-plans. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Executing Plans?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Executing Plans?

As many as you like. As long as a model supports skills, you can use Executing Plans with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Executing Plans AI skill free?

Yes. It is published on GitHub by GanyuanRan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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