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Step Implementation

Community
Dark-Alex-17
step-implementation

End-to-end protocol for executing one step of a phased implementation plan - orient, staleness check, checklist, implement, edge-case sweep, verify, review, handoff, approval. Grants shell access for build/test commands.

Overview

PublisherDark-Alex-17
Repositorycoyote
Skill namestep-implementation
Stars
56
Forks
2
Bundled files
Instructions only
LicenseAGPL-3.0
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 Dark-Alex-17 on GitHub. Read the source before you install it.

Installation

Install the Step Implementation 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/Dark-Alex-17/coyote/tree/main/assets/skills/step-implementation
  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/Dark-Alex-17/coyote.git /tmp/coyote
mkdir -p .claude/skills
cp -r /tmp/coyote/assets/skills/step-implementation .claude/skills/step-implementation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Step Implementation 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 Step Implementation 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 Step Implementation 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.

You are executing ONE step of a phased implementation plan. Previous steps were executed in sessions you cannot see; later steps depend on what you do and document. The protocol below is ordered — do not skip phases, do not reorder them.

Companion skills: load handoff-protocol before Phase 1 (you must READ a handoff correctly) and keep it loaded for Phase 8 (you must WRITE one). Load verification-gates for Phase 6. The plan schema is defined in plan-authoring.

Phase 1 - Orient

  1. Read the previous step's handoff (plans/handoffs/, highest step number below yours). If none exists, you are step 1.
  2. Read the current step plan (plans/steps/). Note its depends_on — confirm those steps' handoffs exist and report success. If a dependency failed or is missing, STOP and escalate via user__ask.
  3. Read plans/NOTES.md for durable facts discovered by earlier steps.
  4. Apply anything the previous handoff directed at your step (approved plan updates, warnings).
  5. Set the plan's frontmatter status: in-progress.

Phase 2 - Staleness check (BEFORE any edit)

The plan was written before steps 1..N-1 changed the codebase. Verify its assumptions still hold:

  • Grep the symbols the plan references — do they still exist, with the claimed signatures?
  • Read the plan's Context snippets at their claimed locations — has the code drifted?
  • Confirm the Test commands still work.

Discrepancies are deviations — handle them via Phase 5's protocol BEFORE implementing. Executing a stale plan literally is the primary failure mode of phased work.

Phase 3 - Checklist

todo__init with the step objective, then one todo__add per task in the plan's Tasks section, in order. Append the protocol's own gates as todos: edge-case sweep, verify, review, handoff. Mark items done with todo__done as you go — never batch. The checklist is what survives context compression; keep it truthful.

When you spawn an agent whose session you may need to resume, embed its session_id in the corresponding todo item text ("Implement task 3 (coder <session_id>)"). A fix or follow-up to a child's work resumes that child's session_id; a fresh spawn is for new work. If your context gets compressed mid-step, the plan repo tells you WHAT the step is and the todo list tells you WHERE you are and WHICH sessions to resume; re-orient from those, not from the summary's recollection.

Phase 4 - Implement

  • Implement ONLY what the plan's Tasks and Objective ask. Out of scope means out of scope.
  • Follow the patterns pasted in the plan's Context. When plan and current codebase disagree, the codebase wins — record the deviation.
  • Write tests from the plan's Acceptance criteria, not from your implementation. Criteria-first tests catch what tautological tests cannot.
  • While in the code, note (do not fix) anything the planning exploration missed — feed it to Phase 5.

Phase 5 - Edge-case sweep and deviations

Edge cases. For each edge case you discovered: if it belongs to THIS step, handle it (or punt explicitly in the handoff with a reason). If it belongs to a LATER step, check that step's plan — if the plan already covers it, done; if not, add it to that plan's Edge cases section and record the addition in your handoff.

Deviations. Classify each:

ClassDefinitionAction
MinorSame objective and scope, mechanics differ (renamed symbol, moved file, extra helper)Resolve it, document in handoff
MajorChanges scope, approach, interfaces, or invalidates a later step's assumptionsDo NOT silently proceed. Either escalate via user__ask, or write a proposed downstream-plan diff into the handoff per handoff-protocol

Never rewrite a later step's Objective, Tasks, or Out of scope directly — edge-case annotations are the only direct downstream edit you may make.

Phase 6 - Verify (order matters)

  1. Formatter (if configured) — format BEFORE collecting evidence, so evidence reflects final code.
  2. Linter (if configured) — fix findings your change introduced.
  3. Build/typecheck — exit code 0.
  4. FULL test suite — not just your new tests; regressions in untouched code are your problem if your change caused them.

Capture commands and exit codes verbatim — they go in the handoff as evidence. Pre-existing failures: note explicitly, don't fix, don't hide. Apply the 3-strike rule: after 3 failed fix attempts, stop, revert to working state, escalate.

Phase 7 - Review

Self-review the diff with code-review + ai-slop-remover loaded. For broad steps (5+ files or crossing architectural boundaries), request an independent pass (code-reviewer agent) instead. Fix blockers; re-run Phase 6 after any fix.

Phase 8 - Handoff

Gate: every todo is either done or explicitly deferred with a reason. No silent drops.

Write the handoff per handoff-protocol — schema, pasted evidence, deviations, downstream updates, notes for the next step. Append durable, step-independent facts to plans/NOTES.md. Set the plan's frontmatter status: complete.

Phase 9 - User approval

Present: what was done, deviations, downstream plan changes (made or proposed), evidence summary, handoff location. Then STOP — do not begin the next step. If the user requests changes, address them, re-run Phase 6, update the handoff, and present again.

Anti-patterns

  • Editing code before the staleness check — the primary source of mid-step surprises
  • Implementing "while I'm here" improvements outside the plan's scope
  • Tests derived from the implementation instead of the acceptance criteria
  • Collecting build/test evidence BEFORE formatting/linting, then shipping different bytes
  • Running only your new tests and claiming "tests pass"
  • Silently absorbing a major deviation instead of escalating or proposing a plan diff
  • Rewriting downstream plan scope directly instead of proposing per handoff-protocol
  • Starting the next step without user approval

Frequently asked questions

What does the Step Implementation AI skill do?

End-to-end protocol for executing one step of a phased implementation plan - orient, staleness check, checklist, implement, edge-case sweep, verify, review, handoff, approval. Grants shell access for build/test commands.

Why use Step Implementation on TypingMind?

Because you install it once and use it with any model. Step Implementation 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 Step Implementation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dark-Alex-17/coyote/tree/main/assets/skills/step-implementation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Step Implementation?

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 Step Implementation?

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

Is the Step Implementation AI skill free?

Yes. It is published on GitHub by Dark-Alex-17 under the AGPL-3.0 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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