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Write Phase Plan

Organization
hraness
write-phase-plan

Writes or restructures an implementation plan in the format the phase-orchestrator skill consumes: dependency-ordered phases with explicit scope, acceptance criteria, validation commands, and status/log conventions. Use when the user asks to write a plan for phase orchestration, prepare a plan or PRD for delegated multi-agent implementation, or convert a spec, checklist, or design doc into implementable phases.

Overview

Publisherhraness
Repositoryslopcamera
Skill namewrite-phase-plan
Stars
7
Forks
1
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Write Phase 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/hraness/slopcamera/tree/main/.agents/skills/write-phase-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/hraness/slopcamera.git /tmp/slopcamera
mkdir -p .claude/skills
cp -r /tmp/slopcamera/.agents/skills/write-phase-plan .claude/skills/write-phase-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Write Phase 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 Write Phase 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 Write Phase 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.

Write Phase Plan

Goal

Produce a plan document that a phase-orchestrator run can execute without re-deriving structure. Each phase must be implementable by a worker that has no chat history: the phase section is the worker's entire brief, plus repo rules and the code itself.

The plan is the source of truth during the run. The orchestrator updates phase status and appends log entries as it goes, so the document must be written to absorb that.

Where The Plan Lives

Follow the repo's convention for design docs (for example plans/<author>/ in repos that define one). One markdown file per plan. Link out to the PRD/spec rather than duplicating it.

Document Structure

markdown
# <Feature name>

## Overview
One or two paragraphs: what is being built and why. Link the PRD/spec.

## Constraints
Repo-wide facts every phase needs: commit/PR policy, validation commands,
branch or worktree requirements, anything the orchestrator must not infer.

## Phases
A short table or list: phase ID, name, depends-on, parallelizable-with.

## Phase 1: <name>
- **Status:** Not started
- **Depends on:** none | Phase N
- **Objective:** one sentence, the outcome not the activity.
- **Scope:** the files/modules this phase owns (its write scope).
- **Out of scope:** adjacent work this phase must not touch.
- **Approach:** implementation notes — key decisions already made, pointers
  to the code the worker should read first, known landmines.
- **Acceptance criteria:** checkable statements (see below).
- **Validation:** exact commands to run, plus any manual checks.

## Phase 2: <name>
...

## Implementation log
(Empty at authoring time. The orchestrator appends one entry per phase:
date, phase, summary, validation results, review outcome, commit SHAs,
deviations, remaining risks.)

Sizing And Ordering Phases

  • One phase = one implementer worker session. If a phase's Approach section needs subheadings to stay coherent, split it.
  • Order by dependency, foundations first. A schema/migration phase is always its own phase (and in many repos its own PR — record that in Constraints).
  • Phases that can run in parallel must have disjoint write scopes. Declare parallelizability explicitly in the phase list; the orchestrator will not guess.
  • If a phase exists only to set up a later phase, say which one, so the orchestrator knows a deviation there propagates.

Writing For A Context-Free Worker

The implementer sees the plan, the repo, and nothing else. So:

  • Use standalone spec language. No "as discussed", "still", "instead of the old approach", or references to conversations that produced the plan.
  • Name concrete things: file paths, function names, commands — not "the relevant helper" or "the usual checks".
  • State decisions as decisions. If something is genuinely unresolved, mark it as an explicit open question and say who resolves it (the worker may decide in-scope questions; the orchestrator escalates cross-cutting ones).
  • Put shared context in Constraints once rather than repeating it per phase.

Acceptance Criteria

Each criterion must be checkable by the reviewer from the diff and the running code, without asking the author:

  • Good: "POST /orgs/:id/invites returns 403 for a non-admin member; covered by a route test."
  • Bad: "Invites are properly secured."

Include the negative space: what must not change (existing behavior, public contracts, performance characteristics) when regression there is a real risk.

Status And Log Conventions

  • Phase status vocabulary: Not started, In progress, Done, Partial, Blocked.
  • The orchestrator owns status transitions and log entries during the run; the author sets everything to Not started.
  • Keep the Implementation log section present even when empty, so run updates have a stable place to land.

Anti-Patterns

  • Phases split by activity (design/build/test) instead of by deliverable — every phase should end with validated, committable work.
  • Two phases that edit the same files marked as parallel.
  • Acceptance criteria that restate the objective instead of testing it.
  • A plan that embeds the whole PRD — link it and keep the plan operational.
  • Hidden sequencing: prose that implies an order the phase list doesn't state.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Write Phase Plan AI skill do?

Writes or restructures an implementation plan in the format the phase-orchestrator skill consumes: dependency-ordered phases with explicit scope, acceptance criteria, validation commands, and status/log conventions. Use when the user asks to write a plan for phase orchestration, prepare a plan or PRD for delegated multi-agent implementation, or convert a spec, checklist, or design doc into implementable phases.

Why use Write Phase Plan on TypingMind?

Because you install it once and use it with any model. Write Phase 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 Write Phase Plan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hraness/slopcamera/tree/main/.agents/skills/write-phase-plan. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Write Phase 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 Write Phase Plan?

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

Is the Write Phase Plan AI skill free?

Yes. It is published on GitHub by hraness 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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