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Plan Authoring

Community
Dark-Alex-17
plan-authoring

Author executable high-level plans and per-step implementation plans for phased work. Defines the plan repo layout and step-plan schema. Grants filesystem access for grounding plans in real code.

Overview

PublisherDark-Alex-17
Repositorycoyote
Skill nameplan-authoring
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 Plan Authoring 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/plan-authoring
  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/plan-authoring .claude/skills/plan-authoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan Authoring 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 Plan Authoring 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 Plan Authoring 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 writing implementation plans that a DIFFERENT agent will execute later, in a fresh session, with zero access to this conversation. The plan IS the executor's entire context. A plan that needs the conversation to make sense is a broken plan.

Plan repo layout

Default layout (match the existing layout instead if the repo already has one):

plans/
  plan.md            # high-level plan; links each step plan
  steps/01-<slug>.md # one file per step, numbered in execution order
  handoffs/          # written by executors; see `handoff-protocol`
  NOTES.md           # rolling durable facts discovered during execution

In plan.md, link each step plan with an inclusion link (the link alone on its own line). This makes the plan repo an IWE hierarchy — agents navigating a large plan corpus can load iwe-knowledge-base and traverse it structurally instead of globbing.

High-level plan requirements

  • Ordered list of steps. Each step is independently implementable and independently verifiable — it compiles and its tests pass WITHOUT any later step existing.
  • The dependency graph is explicit and acyclic. If step 4 needs step 2's API, step 4's plan says so.
  • Steps are sized for one focused session: roughly 1-5 files of meaningful change. A step that needs "and then also..." is two steps.
  • State what the plan does NOT cover. Scope creep starts where scope boundaries are implicit.

Step plan schema

Every step plan starts with frontmatter:

yaml
---
step: 3
title: Add retry policy to the fetch client
depends_on: [1, 2]
status: pending   # pending | in-progress | complete
---

And contains these sections, all mandatory:

SectionContents
Objective1-3 sentences: what exists after this step that didn't before
ContextFile paths AND pasted code snippets (5-20 lines) showing the patterns to follow. Not just paths — actual code
TasksOrdered, atomic tasks. Each maps to one todo item for the executor
Acceptance criteriaMeasurable behaviors. These become the tests
Test commandsExact commands to run, from the repo root
Edge casesKnown edge cases this step must handle or explicitly punt on
Out of scopeWhat the executor must NOT touch, even if tempting

Names a stranger can read

Every new module, type and public function the plan or breakdown names must be meaningful to a reader who has not read the plan. Coinages and acronyms the plan invented get a descriptive name BEFORE tasks are created: a name that reaches 30 files is never questioned again, and the implementer will not stop to rename what the plan told them to type.

  • Bad: mod lrq (the design doc's shorthand for the transport's request/response pair).
  • Good: mod link_request.

Documents, directories and scaffolding a plan adds

Every document, directory, example target, dependency added ahead of use, or smoke test the plan adds must name its lifetime -- permanent, or replaced by <X> at <milestone> -- AND the task that removes it. Record this in the plan as a table under a section named ## Added artefacts:

ArtefactLifetimeRemoved by
tests/support/smoke_deps.rsreplaced by the transport client at step 4step 4
docs/link-request.mdpermanent--

Test-only binaries live under tests/ support (or are declared as test targets in the build manifest with a comment saying so), never under examples/ as if they were examples. Scaffolding tasks ("add dependencies ahead of use", "smoke the deps") must name the later task that supersedes them, and that task's acceptance criteria include deleting the scaffolding.

Writing for a context-free executor

  • Paste code snippets from your exploration into Context. "Follow the pattern in foo.rs" forces the executor to re-do exploration you already did.
  • Use repo-relative paths from the project root. Never "the file we discussed."
  • Name symbols exactly: RetryPolicy::backoff, not "the backoff logic."
  • If a decision was made in discussion (X over Y), record the decision AND the one-line reason in the PLAN FILE (or the task's log once execution starts) -- never in CONTRIBUTING, README or code comments. The executor will face the same fork and must not re-litigate it.
  • Write acceptance criteria as observable behavior ("returns 429 after 3 failed attempts"), not implementation ("uses a for loop"). Criteria that describe implementation produce tautological tests.

Grounding (before the plan is done)

Plans rot when written from memory. Before finalizing each step plan:

  1. fs_grep every symbol the plan references — confirm it exists and is spelled right.
  2. fs_read the files listed in Context — confirm the pasted snippets are current.
  3. Confirm the test commands actually exist (check justfile, Makefile, package.json scripts, CI config).

A plan referencing a function that doesn't exist fails the executor at the worst possible time: mid-implementation.

Edge cases are a first-class section

For every step, enumerate the edge cases you can foresee: empty inputs, concurrent access, error paths, partial failures, migration/compat concerns. If an edge case belongs to a LATER step, write it in that step's plan now — not in a comment, not in your head. Executors are instructed to propagate newly discovered edge cases downstream; make their diff small by having the section exist.

Anti-patterns

  • "As discussed above" / "per our conversation" — the executor has no conversation
  • File paths without pasted snippets in Context — forces re-exploration
  • Acceptance criteria like "works correctly" — unmeasurable, untestable
  • A step that depends on a later step — cycle; re-order or merge
  • Omitting Out of scope — the executor will helpfully refactor things you didn't ask for
  • Frontmatter without depends_on or status — breaks status queries and dependency checks
  • A plan coinage carried into code -- the module is named for a shorthand nobody outside the plan recognises
  • An added document, directory or example without a lifetime -- it outlives the thing it scaffolded
  • A scaffolding task with no superseding task named -- "add deps ahead of use" with nothing scheduled to delete it
  • Decisions recorded in CONTRIBUTING or README -- they belong in the plan file or the task log

Frequently asked questions

What does the Plan Authoring AI skill do?

Author executable high-level plans and per-step implementation plans for phased work. Defines the plan repo layout and step-plan schema. Grants filesystem access for grounding plans in real code.

Why use Plan Authoring on TypingMind?

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

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

Which AI models can use Plan Authoring?

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 Plan Authoring?

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

Is the Plan Authoring 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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