Author Stepwise Skill logo

Author Stepwise Skill

Organization
PostHog
author-stepwise-skill

Scaffold a new context-mill skill that walks an agent through an ordered series of narrowly-scoped reference files. Works for any number of steps. Use when the user wants to add a docs-only skill where the agent must read each step in strict order, one at a time, with each step isolated, with clear endpoints, and no prefetching.

Overview

PublisherPostHog
Repositorycontext-mill
Skill nameauthor-stepwise-skill
Stars
69
Forks
21
Bundled files
Instructions only
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 PostHog on GitHub. Read the source before you install it.

Installation

Install the Author Stepwise Skill 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/PostHog/context-mill/tree/main/.claude/skills/author-stepwise-skill
  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/PostHog/context-mill.git /tmp/context-mill
mkdir -p .claude/skills
cp -r /tmp/context-mill/.claude/skills/author-stepwise-skill .claude/skills/author-stepwise-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Author Stepwise Skill 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 Author Stepwise Skill 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 Author Stepwise Skill 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.

This skill scaffolds a multistep skill. A docs-only context-mill skill made of an ordered chain of reference files. The agent reads one file at a time and never sees later files until it finishes the current one.

When this shape is right

This shape gives you three things:

  • Bounded context per step. The agent reads one focused file at a time. Token use stays small. Attention stays on the current goal. The shape works the same for two steps or twenty.
  • A clean checkpoint per step. Each step finishes when its verifiable objective completes. Progress is observable. You can pause, check the run is on track, and resume from the next file.
  • Constrained scope. The agent acts only on what it has been shown. It does not preload future work, branch, or chase tangents.

Use the multistep pattern when all of these are true:

  • The work is sequential. Step N depends on step N-1's output.
  • Each step has a narrow job. If you can describe step N in one paragraph, it is narrow enough.
  • The skill is docs-only. There is no example project to bundle.

If the skill is just a bag of related references that the agent can read in any order, do not use this shape. Use a flat docs-only skill. Extra ceremony hurts.

The mechanics that follow (references.preamble, per-file next_step, "do not Glob/ls/find", "do not preload", "do not re-read earlier files") make progressive disclosure the path of least resistance. Do not weaken them. A step that says "for context, also read X" defeats the pattern.

Authoring each step

Keep the scope tight:

  • One job per step. The step's # Heading should name a single goal. If you need "and" or a comma, it is two steps.
  • One verifiable output. Every step ends with a specific, verifiable artifact. A single tool call. A written file. A returned value. "Investigate X" is not an output. "Write the result of investigating X to <location>" is. You should be able to verify that the step has completed and is correct by some condition, test, or tool call.
  • A one-paragraph fence at the top. State the goal. List what this step does NOT do, naming the later step that owns the deferred work. Do not describe how that step will solve it.
  • Persist anything later steps need. If step 2 needs a value step 1 produced, step 1 must write it somewhere step 2 can read on its own. The preamble forbids re-reading earlier files.
  • No optional side quests. Cut "you may also want to…", "if you have time…", "for completeness…". If it matters, it is a step. If it does not, drop it.
  • Stay short. A step that grows past a screen or two is doing too much. Split it. Long steps invite scan-skimming and tangents.

If you find yourself wanting to add "context" or "background" to help the agent decide, the step is not tight enough. Decide for the agent at authoring time. Do not push the decision into the run.

Directory layout

context/skills/<group>/
├── config.yaml
├── description.md          # SKILL.md template (entry point)
└── references/
    ├── 1-<step-name>.md    # frontmatter: next_step: 2-<step-name>.md
    ├── 2-<step-name>.md    # frontmatter: next_step: 3-<step-name>.md
    ├── …
    └── N-<step-name>.md    # frontmatter: next_step: null  (terminal)

<group> is the directory name. With a single id: all variant it also becomes the generated skill ID.

config.yaml

yaml
type: docs-only
template: description.md
description: <one-line description for SKILL.md frontmatter>
tags: [<tag>]
references:
  preamble: "**Read ONLY this file.** Do not read any other reference file until this one tells you to."
shared_docs: []        # optional. PostHog doc URLs every step can rely on.
variants:
  - id: all
    display_name: <human title>
    tags: [<tag>]
    docs_urls: []

The build injects references.preamble after the first # Heading of every reference file that has a next_step. It also appends a **Upon completion, continue with:** [<next>](<next>) link at the bottom. The terminal file (with next_step: null) gets neither.

Reference file frontmatter

Non-terminal step:

markdown
---
next_step: 2-<step-name>.md
---

# Step 1: <narrow goal>

<one paragraph: what this step does, what it does NOT do, and what the previous step has already settled.>

…step body…

Terminal step:

markdown
---
next_step: null
---

# Step N: <final goal>

…step body…

Notes:

  • next_step is the filename relative to references/. Not a path.
  • Always declare next_step. A file with no frontmatter is treated as a standalone reference (no preamble, no link). Fine for one-off references, wrong for a chain.
  • Do not hand-write the "Upon completion, continue with" line. The build appends it.

description.md (SKILL.md template)

The entry point. Keep it short. The real work lives in the reference chain. It must:

  • Explain the skill's purpose in one or two paragraphs.
  • Tell the agent to start by reading references/1-<step-name>.md by exact path. Forbid Glob, ls, and find on the skill directory. Forbid preloading future steps.
  • Describe any cross-cutting state or conventions every step depends on. Where intermediate values live. What the final output is.
  • Declare the [STATUS] and [ABORT] conventions (see next section) so each step can use them.
  • End with {commandments} to inherit the framework guidelines for the skill's tags.

Status and abort lines

The wizard runner reads two prefixed line patterns from the agent's output:

  • [STATUS] <message> updates the live "Working on…" banner. Use these often. They are cheap. Each step file should list the exact [STATUS] strings to emit at each sub-step (for example, [STATUS] Scanning manifests, [STATUS] Writing report).
  • [ABORT] <reason> terminates the run. The runner catches this and stops. Use it for unrecoverable preconditions (no SDK found, missing credentials, etc.). The agent does not need to halt itself after emitting [ABORT].

Declare both in description.md so every step can rely on them. Then list the specific [STATUS] strings (and any [ABORT] reasons) inline in each step file at the points they apply. Existing skills like revenue-analytics/ and quack/ show the shape.

Build & verify

bash
npm run build

Unzip dist/skills/<group>.zip and confirm the chain is intact: non-terminal references have the preamble and continuation link, the terminal has neither.

bash
npm test

Should stay green. The reference-folder test (scripts/lib/tests/skill-generator-references-folder.test.js) covers the copy path.

Anti-patterns

  • Single-file "multistep" skills. If your reference folder has one file, this is not a chain. It is a flat docs-only skill. See revenue-analytics/ and quack/ for shape.
  • Branching chains. next_step is a single filename. If a step has two follow-ups, split into two skills, or have step N enumerate the choices and let step N+1 handle both.
  • Frontmatter on flat (non-chain) groups. Per-framework groups like error-tracking/ and feature-flags/ do not use references/ chains. Use this shape only when sequencing matters.

Example: a 2-step skill

A minimal chain has two files: an opener and a terminal step. The shape generalizes to N steps by inserting more files in the middle.

Layout

context/skills/example-stepwise/
├── config.yaml
├── description.md
└── references/
    ├── 1-discover.md
    └── 2-emit.md

config.yaml

yaml
type: docs-only
template: description.md
description: Two-step example skill. Discover, then emit.
tags: [example]
references:
  preamble: "**Read ONLY this file.** Follow its contents in sequence. Do not read any other reference file until this one tells you to."
shared_docs: []
variants:
  - id: all
    display_name: Example stepwise skill
    tags: [example]
    docs_urls: []

description.md (becomes SKILL.md)

markdown
# Example stepwise skill

This skill walks the agent through a two-step task. First discover the target. Then emit the result.

**Start by reading `references/1-discover.md`.** Do not Glob, ls, or find the skill directory. Do not preload `2-emit.md`.

Each step persists its output to `<known-location>` so the next step can read it without re-opening earlier step files.

## Status

Report progress with `[STATUS]` prefixed messages. Each step lists the exact strings to emit.

## Abort

Report unrecoverable failures with `[ABORT] <reason>`. The runner terminates the run. Do not halt yourself.

- No target found

## Reference files

{references}

## Framework guidelines

{commandments}

references/1-discover.md

markdown
---
next_step: 2-emit.md
---

# Step 1: Discover the target

This step locates the target and writes its identifier to `<known-location>`. It does NOT format, transform, or emit the result. That belongs to step 2.

Emit `[STATUS] Locating target` at the start of the search. If no target exists, emit `[ABORT] No target found` and stop.

…step body, ending with a single tool call or write that produces the target identifier…

references/2-emit.md

markdown
---
next_step: null
---

# Step 2: Emit the result

The target identifier is at `<known-location>` (written by step 1 into `targets.json`). This step formats and emits the result into a markdown file. Do not re-read step 1.

…step body, ending with the final emission…

Frequently asked questions

What does the Author Stepwise Skill AI skill do?

Scaffold a new context-mill skill that walks an agent through an ordered series of narrowly-scoped reference files. Works for any number of steps. Use when the user wants to add a docs-only skill where the agent must read each step in strict order, one at a time, with each step isolated, with clear endpoints, and no prefetching.

Why use Author Stepwise Skill on TypingMind?

Because you install it once and use it with any model. Author Stepwise Skill 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 Author Stepwise Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/PostHog/context-mill/tree/main/.claude/skills/author-stepwise-skill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Author Stepwise Skill?

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 Author Stepwise Skill?

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

Is the Author Stepwise Skill AI skill free?

It is published on GitHub by PostHog. Check the repository for licensing terms. 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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