Create Implementation Plan logo

Create Implementation Plan

Organization
eai-org
create-implementation-plan

Turn a refined requirements document into a structured implementation PLAN.md a fresh session can execute. Planning only — decides the "how", not the "what". Invoke manually only.

Overview

Publishereai-org
Repositoryagent-toolkit
Skill namecreate-implementation-plan
Stars
80
Forks
18
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 eai-org on GitHub. Read the source before you install it.

Installation

Install the Create Implementation Plan 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/eai-org/agent-toolkit/tree/main/skills/create-implementation-plan
  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/eai-org/agent-toolkit.git /tmp/agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit/skills/create-implementation-plan .claude/skills/create-implementation-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Implementation Plan 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 Create Implementation Plan 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 Create Implementation Plan 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.

Create Implementation Plan

Planning only — no code changes, no execution. You produce one document: an implementation plan that is the contract for a later execution session.

This is the "how", not the "what". The "what" was settled in an earlier refinement step and lives in the requirements document you're given; do not redefine scope. But you must flag any gap, ambiguity, or inconsistency you find in the requirements — surface it, never paper over it.

Golden rule

Never guess — ask. With one limit: anything resolvable by reading the codebase, resolve by reading the codebase; only questions the code cannot settle go to the user.

Steps

Your task is to produce the implementation plan. The steps below build toward it; grilling the user — interviewing relentlessly to resolve anything the code can't settle — is woven through the design, not a separate phase that runs before planning starts.

  1. Read the requirements document the user references (e.g. a *.REQUIREMENTS.md). If the path is ambiguous, ask. Its Open questions are forks to grill in step 3, each with its tentative answer as the recommendation; none stays open in the plan. Its Overrides carry over into part 2's Overrides.

  2. Verify against the actual codebase. Open the files the requirements cite and confirm the prior-art references still hold; note anything that has shifted since the requirements were written. The requirements may also carry verified codebase facts — use any that are there to save re-discovery work, aware the code may have moved meanwhile (a pinned commit makes the check cheap: git diff <commit>..HEAD -- <cited paths>). If the requirements gate implementation on missing data or upstream work, verify that gate independently — stale gating claims are a common failure mode and easily inflate into a plan's first step when the data is in fact already addressable.

  3. Work out the approach. This is the core of the task: design the "how" — what existing code to reuse, what to introduce, where each change goes, and the order of operations that avoids broken intermediate states (data model before its consumers, code before its tests, a bug's regression test aside: see the Plan file's Steps). Track dependencies between steps. Working this out surfaces the decision tree: the forks where more than one reasonable approach exists. Typical forks to design through (and grill on when the code can't settle them): whether to refactor existing code to reuse it or build anew; which API or interface to call; which unit tests to add; code style, file names, and folder structure.

    Docs generally outrank prior art. A mirrored analogue doesn't override a documented rule, and a rule's rationale doesn't license exceptions to it — existing code may predate the rule. When docs and prior art conflict and you're unsure how to resolve it, ask the user; a deviation the user approves is recorded in the plan's Overrides section, so the execution session knows it's deliberate.

    Prior art models shape, not content. Its settings fit its own runtime, tooling and purpose and may predate the current ones, so before a step reuses a file, check each setting against the new component's target and prescribe only what fits, citing the file for shape; "copy X verbatim" is a decision settled line by line, never by the file's existence. A technical claim a step rests on ("X needs exactly Y") is verified against a doc, a compile or a test, never read off what the prior art happens to do.

    Deletions tend to cascade. When a step deletes code, look for what that code was the last user of (a module, helper, template, constant, package) by searching for each one's other users, and consider naming it for deletion too, repeating for every piece newly orphaned; one the search cannot clear (an export, a name resolved at runtime) is a fork to grill.

    Walk every point a run can stop when steps change live state in stages (a deploy, a migration, a swap): each step, an abort, a timeout, a lost worker, an automatic re-run or restart. At each, settle what the target is left in and what the log, notifications and any manual-fix advice say there. Hazards recorded in related plans' decisions logs are prior art.

    Grill the user along the way. Whenever the approach hits a fork you can't settle from the code, stop and resolve it with the user before continuing — don't guess, and don't defer the decision into the plan. Interview relentlessly until you reach shared understanding, walking each branch of the decision tree and resolving dependencies between decisions:

    • One question at a time, each with your recommended answer, worded so that "yes" accepts it, via explain-in-simple-language when available.
    • Facts are yours to find (codebase, docs, tool output): look them up, never ask. Decisions are the user's: put each one to them and wait.
    • Cover every gap, ambiguity, or inconsistency surfaced while reading, verifying, and designing.
  4. Write the plan (below) — once the approach is settled, no open question remains, every step serves an AC or a convention, and identifiers and signatures match across steps.

Plan file

The output must be self-contained and ready for a fresh session that reads only the plan file and starts implementing — it should not need to read the requirements document or the ticket. This is the single most important constraint. Anything the execution session needs (acceptance criteria, prior-art citations, file paths, naming/string conventions, type signatures, override notes) must appear in the plan itself. Repetition from the requirements is intentional: the plan is the contract, not a diff against the requirements.

Create the file in the same directory as the requirements document, named by replacing .REQUIREMENTS with .PLAN (e.g. FOO.REQUIREMENTS.md → FOO.PLAN.md). If the input doesn't follow that convention, append .PLAN before .md. <id>-<slug> is the plan filename without .PLAN.md (123-report-approval); the id is absent when the task has no ticket.

Structure — five parts:

  1. Summary — 1–3 sentences: what this plan implements and the overall shape of the change (which areas are touched).

  2. Conventions and overrides — two distinct kinds of cross-cutting context:

    • Conventions: open with the commit the citations were verified at (short hash, dirty-tree note); then rules that apply across all steps — naming patterns, user-facing string rules, the testing approach (what to test, following existing test conventions in the codebase). Copy from the requirements verbatim where applicable.
    • Overrides: one-time corrections where the requirements deliberately diverge from a default or prior assumption. Each entry: what was assumed, what the actual requirement is, and why. The execution session applies each only where it fits.
  3. Steps — ordered implementation steps. Each is concrete enough to act on but doesn't dictate every line, and is one change a fresh session finishes with context to spare; a bigger change is several steps. Each step has:

    • What — a short imperative.
    • Where — concrete file paths and identifiers.
    • How — the approach in prose. Reuse decisions go here, with path:line-range citations for prior art.
    • Snippet — only when prose is genuinely more ambiguous than code (a non-obvious type signature, a tricky nested structure, an unfamiliar call shape). Default to omitting; keep under ~10 lines.
    • Depends on — earlier step numbers this one requires, if any.

    Each step is self-contained — readable without scrolling back. No "as in step 2" without restating what step 2 did. Step 1 must never be discovery work ("find the field names", "explore where this lives") — discovery already happened in the steps above. A small deterministic sanity check of a known fact is fine; open-ended investigation is not.

    When the requirements carry a Reproduction part, step 1 is a regression test or a scripted reproduction asserting the expected behaviour that part records, whether or not the bug was reproduced, so it is not discovery; the fix follows in later steps. It is the smallest test that shows the bug (unit before integration before e2e), a larger one only when the bug lies in how parts connect. Its How says (the executor may know no such rule) that the test must be seen failing on the bug before the fix is written (its assertion, or the error the bug raises; a broken import or fixture is no failure seen), the failure seen is reported in one line, and a test that cannot be made to fail that way stops the run before any fix, with the reason reported. The step writing the fix says to rerun that test afterwards and see it pass.

  4. Acceptance criteria — flat - [ ] AC-n checklist the implementation must satisfy, copied from the requirements' AC section. If unnumbered, number them (AC-1, …). Each references the step number(s) that satisfy it (e.g. "AC-3 → steps 4, 7"). Tag any AC that needs manual verification only (manual — leave unticked, report as pending). The execution session uses this as its done-check and ticks the rest.

  5. Decisions log — a standing instruction copied into the plan for the execution session: "When a settled decision deviates from or extends this plan, append one line — the decision and its why — to <id>-<slug>.DECISIONS.md beside this plan, the moment it's settled." Without it, the why behind mid-implementation pivots is lost to later handover and review.

The plan must capture all the thinking — every decision needed to implement the feature, so the execution session re-derives nothing. It may omit only mechanical, locally-reversible detail with no cross-cutting consequence (a variable name, the exact wording of a log line). Anything that spans files, depends on knowing the codebase, or is costly to get wrong (which utility to reuse, what order to change things in, what new types to introduce) belongs in the plan. When unsure whether something is a decision or a mechanical detail, put it in.

Boundaries

  • Do not modify any source files. The only file you write is the plan.
  • Do not start implementing.
  • When preparing the plan from a *.REQUIREMENTS.md file, you normally shouldn't need to go back to the *.TICKET.md — close any gaps with the user instead. Never assume a requirement; ask.
  • Write the plan to disk only after every open question is resolved.

When done, state that the plan file is ready, by its project-relative path (relative to the working directory, never absolute), and offer these ways forward, each as its own fenced block under the label quoted below, holding a single copy-pasteable launch command — session name and prompt combined, so one paste starts a fresh session with a clean context. Use the agent tool's launch syntax (claude below is only the example) and name the session after <slug>, the plan filename's slug (without id prefix or extension), so the phase is recognizable in the session list:

  1. "Execute the plan:" — reads only the plan file. <validation> is the project's own checks: the commands its governing docs name, else the toolchain's (lint, tests, build — only those that exist); nothing runnable → drop the clause:

    claude --name execute-plan-report-approval "Execute the plan .agents/plans/123-report-approval/123-report-approval.PLAN.md, then run <validation>; report, never commit"
  2. "or split it into tasks first, for small reviewable increments:" — when split-plan-tasks is available:

    claude --name split-plan-tasks-report-approval "/split-plan-tasks .agents/plans/123-report-approval/123-report-approval.PLAN.md"
  3. "or harden it first, so a fresh reviewer challenges it against the ticket and the code before anything is built:" — when harden-artifact is available:

    claude --name harden-plan-report-approval "/harden-artifact .agents/plans/123-report-approval/123-report-approval.PLAN.md"

Then offer the alternative: one line offering to clear the current session (/clear below is only the example; use the agent tool's clear command) and run the next phase here, then the same labelled blocks with the bare prompts.

Frequently asked questions

What does the Create Implementation Plan AI skill do?

Turn a refined requirements document into a structured implementation PLAN.md a fresh session can execute. Planning only — decides the "how", not the "what". Invoke manually only.

Why use Create Implementation Plan on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/eai-org/agent-toolkit/tree/main/skills/create-implementation-plan. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Create Implementation Plan?

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 Create Implementation Plan?

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

Is the Create Implementation Plan AI skill free?

Yes. It is published on GitHub by eai-org 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.

View all

Set up your own AI workspace now

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