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Implementation Planning

Community
neuromechanist
implementation-planning

Use this skill when the user asks to "plan this feature", "write an implementation plan", "design before coding", "think through the approach", "review this plan", or before any multi-file change, architectural decision, experiment with a go/no-go outcome, or strategic pivot. Produces a verifiable plan with pre-registered decision gates and an explicit list of open judgment calls.

Overview

Publisherneuromechanist
Repositoryresearch-skills
Skill nameimplementation-planning
Stars
51
Forks
9
Bundled files
Instructions only
LicenseBSD-3-Clause
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 neuromechanist on GitHub. Read the source before you install it.

Installation

Install the Implementation Planning 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/neuromechanist/research-skills/tree/main/plugins/project/skills/implementation-planning
  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/neuromechanist/research-skills.git /tmp/research-skills
mkdir -p .claude/skills
cp -r /tmp/research-skills/plugins/project/skills/implementation-planning .claude/skills/implementation-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Implementation Planning 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 Implementation Planning 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 Implementation Planning 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.

Implementation Planning

Plans that a different (or weaker) model could execute without re-deriving your reasoning, with success criteria fixed before any result is seen.

Model handoff

Use the strongest available model for problem framing, macro architecture, irreversible decisions, and plan approval. In Codex this is Sol; in Claude it is Fable when available, otherwise Opus. After the architecture is approved, a bounded phase planner may expand it on the intermediate tier (Codex Terra; Claude Sonnet when no design choice remains). Write the resulting brief so a clear-task worker (Codex Luna or Claude Sonnet) can implement it without reconstructing design intent. See agent-fanout for the full routing and escalation policy.

Pick the register (by stakes, not size)

SituationRegister
Hard to reverse after shipping; touches privacy, auth, what data leaves the device, or public contracts; strategic pivotHeavyweight: full plan document, user approval gate before executing (plan mode where available).
Known engineering backlog; the work is clear, there is just a lot of itLightweight: tracked issues carrying the full spec (template below); no separate plan document.
Single ordinary changeNo formal plan; the engineering-loop skill's step order is the plan.

The dividing line is reversibility and blast radius, not effort. A large backlog of clear fixes needs no plan document; a small change to what an external partner receives does.

Heavyweight plan: required sections

markdown
# Plan: <title> (issue #N)
## Context
[The evidence forcing this work, with numbers. What the previous phase or
investigation proved.]
## What already exists to reuse (do not rebuild)
[Named files/functions. Every plan must check this before proposing new code.]
## Approach
[The design, and one paragraph on why this and not the leading alternative.]
## Files
[New and modified, explicit paths.]
## Decision gate (set BEFORE looking at results)
[Crisp pass/fail: "X ships as default if metric A >= threshold on every
case AND metric B does not regress; if it fails, honest conclusion + named
fallback. No adoption of a non-passing candidate."]
## Prerequisites (user actions)
[Only the human can do these: account registrations, credentials, hardware.]
## Agent budget
[How many subagents this plan will spawn, worst case; see agent-fanout cap.]
## Open judgment calls
[Every ambiguity you resolved unilaterally, as a flagged list the reviewer
can veto. Silence on ambiguity is a defect.]
## Verification
[How each deliverable is proven: commands, thresholds, who runs them. For a
behavior-preserving refactor, the FIRST deliverable is the pin/
characterization test captured against the original code, per the
engineering-loop skill.]

Lightweight register: the issue IS the plan

markdown
## Symptom
[User-visible behavior, verbatim if reported.]
## Root cause
[file:line, what is wrong, the fixture/test/commit that proves it.]
## Fix
[Concrete, scoped change. DECIDED POLICY lines for any foreseeable ambiguity.]
Refs: #A, #B.

An implementer (human or agent) executes from the issue alone; that is the test of whether it is written well enough.

For GitHub issue bodies and any other text posted to GitHub (comments, reviews, discussions), keep each paragraph and list item on one source line and separate paragraphs with blank lines. Do not apply sentence- or clause-level semantic line breaks inside a GitHub paragraph. Semantic line breaks remain the default for other prose source.

Non-negotiable planning rules

  1. Verify load-bearing claims. Before presenting any plan, list the 3-5 facts that would break it if wrong, and re-check each one directly against source or live state, even when a trusted subagent supplied them.
  2. Pre-register gates. Success/failure thresholds are written before the experiment or implementation runs. Re-running with adjusted parameters without disclosing that the original gate failed is prohibited.
  3. Check sibling documents. Open issues, ADRs, and plan files in the same tracker may carry constraints on your deliverable that your source document does not restate. Surface contradictions before code starts.
  4. Reuse before rebuild. The "already exists" section is mandatory; check the codebase and past research before proposing new modules.
  5. Separate decisions from details. Batch every decision that needs the user (hard to reverse, quoted to third parties, spends real money or days of compute) into ONE structured question with 2-4 labeled options, one marked "(Recommended)", each with a one-line evidence-based rationale. Decide internal implementation details yourself and state them as facts. If a question times out unanswered, proceed with the recommended option and disclose that at the next checkpoint the user will read ("approving this plan also approves X").
  6. Persist the plan-of-record outside chat. GitHub epic/issue plus a Definition of Done with measurable criteria; make gates self-enforcing where possible (a strict expected-failure test on the exact metric beats a prose checklist). Conversation history will be compacted; the tracker survives.
  7. New information reorders work. When new input contradicts or gates a decision already acted on, stop downstream work, resolve the upstream decision first, state why the order flipped, then resume.
  8. Plans from subagents get verified, not trusted. A planning agent must be read-only, cite file:line for its claims, and end with its own open judgment calls; you then re-verify its load-bearing claims (rule 1) before adopting the plan. Macro architecture uses the lead tier. A bounded phase elaborator may use the intermediate tier only after architecture approval; unresolved judgment returns to the lead.
  9. Kill cleanly. When a gate fails, execute the named fallback: revert prototype code (with authorization), record the dead end (what was tried, what it cost, why it failed) in the tracker and scratch notes, and mark deferred ideas explicitly as deferred, never silently dropped.

Phase sizing (feeds epic-dev)

One phase = one independently reviewable and testable pull request (PR): roughly one subsystem, one migration, or up to ~500 net lines of non-generated change. If the description contains two or more such units, it is an epic (use epic-dev); if not, it is a single feature (engineering-loop). Name phases with 2-3 word kebab-case slugs: drop stopwords, keep the object noun first ("backend-metrics", "sync-engine", not "implement-the-new-backend-metrics").

Frequently asked questions

What does the Implementation Planning AI skill do?

Use this skill when the user asks to "plan this feature", "write an implementation plan", "design before coding", "think through the approach", "review this plan", or before any multi-file change, architectural decision, experiment with a go/no-go outcome, or strategic pivot. Produces a verifiable plan with pre-registered decision gates and an explicit list of open judgment calls.

Why use Implementation Planning on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/neuromechanist/research-skills/tree/main/plugins/project/skills/implementation-planning. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Implementation Planning?

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 Implementation Planning?

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

Is the Implementation Planning AI skill free?

Yes. It is published on GitHub by neuromechanist under the BSD-3-Clause 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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