Evanflow Executing Plans logo

Evanflow Executing Plans

Community
evanklem
evanflow-executing-plans

Execute a written implementation plan task-by-task with inline verification. Stop and ask on blockers. No forced sub-skill chains. Use when a plan exists and you're ready to implement.

Overview

Publisherevanklem
Repositoryevanflow
Skill nameevanflow-executing-plans
Stars
418
Forks
18
Bundled files
Instructions only
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 evanklem on GitHub. Read the source before you install it.

Installation

Install the Evanflow 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.
    https://github.com/evanklem/evanflow/tree/main/skills/evanflow-executing-plans
  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/evanklem/evanflow.git /tmp/evanflow
mkdir -p .claude/skills
cp -r /tmp/evanflow/skills/evanflow-executing-plans .claude/skills/evanflow-executing-plans
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evanflow 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 Evanflow 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 Evanflow 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.

EvanFlow: Executing Plans

Vocabulary

See evanflow meta-skill. Key terms: vertical slice, behavior through public interface.

When to Use

  • A plan file exists (docs/plans/... or wherever the user put it)
  • Ready to start implementation

The Flow

0. Parallelization Check (offer coder-overseer if applicable)

Before doing anything else, scan the plan: does it have 3+ truly independent units that share a common interface contract?

If YES — offer evanflow-coder-overseer to the user before starting sequential execution:

"This plan has [N] independent units. We can execute sequentially or via evanflow-coder-overseer (parallel coders + per-coder overseers + integration overseer + executable cohesion contract via named integration tests). Parallel is faster and adds independent QA. Which path?"

If the user picks coder-overseer, hand off there and stop this skill. If the user picks sequential — or if the plan doesn't qualify (tightly sequential dependencies, fewer than 3 units, no shared contract) — proceed to step 1.

1. Load and Critically Review

  • Read the plan in full
  • Identify ambiguities, gaps, or assumptions you'd make differently
  • If anything is unclear or wrong, raise it with the user before starting
  • Set up TaskCreate items mirroring the plan's tasks

2. Execute Task-by-Task

For each task:

  1. Mark the task in_progress
  2. Follow the plan's bite-sized steps exactly
  3. For code steps, default to TDD via evanflow-tdd (skip only when plan flags an exception)
  4. Run the inline verifications the plan specifies
  5. Run the project-wide quality checks before claiming done. The exact commands are project-specific — find them in CLAUDE.md or the project's README. Typically:
    • typecheck (e.g., tsc --noEmit, pnpm typecheck, cargo check, go vet ./...)
    • lint (e.g., eslint ., pnpm lint, cargo clippy, ruff check .)
    • test for affected workspaces (e.g., pnpm test, pytest, cargo test, go test ./...)
  6. Mark the task completed

3. Handle Blockers

Stop immediately when:

  • Missing dependency or environment problem
  • Plan instruction is ambiguous or contradicts the code
  • Verification fails repeatedly
  • Discover the plan's assumption was wrong

Don't force through. Surface the blocker and ask. If the plan needs revision, return to evanflow-writing-plans.

4. Iterate, Then Stop

After all tasks pass quality checks, hand off to evanflow-iterate for the self-review loop. Iterate finds issues a single pass missed (dead code, naming, scope creep, missing tests). For UI changes, iterate also views the rendered page.

When iterate converges: report what was done and STOP. Do NOT stage files (git add), do NOT commit, do NOT push, do NOT propose integration. The user decides every step from here.

A good "done" report includes: what files changed (one line each), what behaviors were added/changed, what verifications passed. Then await user direction.

Hard Rules

  • Never auto-commit, never auto-stage, never auto-finish. Period. After all tasks pass and iterate converges, report and stop. The user decides what comes next.
  • No forced sub-skill chain. Subagent-driven execution and git-worktree isolation are escape hatches for huge plans, not requirements.
  • Verify before claiming done. Run the project's quality checks and confirm output before marking a task complete or reporting success.
  • Match scope. Don't add features or refactor beyond what the plan calls for.

Hand-offs

  • All tasks complete → evanflow-iterate (self-review loop) → report and STOP
  • Plan needs revision → back to evanflow-writing-plans
  • Plan has 3+ truly independent parallel tasks with a shared contract → switch to evanflow-coder-overseer (parallel coders + per-coder overseers + integration overseer)
  • Working in a shared codebase, want isolation → consider a git-worktrees workflow if your environment provides one

Frequently asked questions

What does the Evanflow Executing Plans AI skill do?

Execute a written implementation plan task-by-task with inline verification. Stop and ask on blockers. No forced sub-skill chains. Use when a plan exists and you're ready to implement.

Why use Evanflow Executing Plans on TypingMind?

Because you install it once and use it with any model. Evanflow 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 Evanflow Executing Plans in TypingMind?

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

Which AI models can use Evanflow 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 Evanflow Executing Plans?

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

Is the Evanflow Executing Plans AI skill free?

It is published on GitHub by evanklem. Check the repository for licensing terms. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇