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Incremental Delivery

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UnpaidAttention
incremental-delivery

Build large multi-part deliverables — multi-file projects, migrations, long documents, N-of-the-same-thing work — in verified increments with dependency ordering, checkpoints, a running state note, and consistency sweeps. Trigger this when a task will produce 3+ files, spans more sub-deliverables than fit in one pass, involves repeating a pattern across many sites (endpoints, parsers, pages, records), or is explicitly requested to be done incrementally or resumably. Do NOT trigger for single-file changes (implementation-standards suffices) or for the initial breakdown of an ambiguous request (use task-planning first — this skill governs execution after the plan exists).

Overview

PublisherUnpaidAttention
Repositoryfable5-methodology
Skill nameincremental-delivery
Stars
94
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 UnpaidAttention on GitHub. Read the source before you install it.

Installation

Install the Incremental Delivery 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/UnpaidAttention/fable5-methodology/tree/main/skills/incremental-delivery
  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/UnpaidAttention/fable5-methodology.git /tmp/fable5-methodology
mkdir -p .claude/skills
cp -r /tmp/fable5-methodology/skills/incremental-delivery .claude/skills/incremental-delivery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Incremental Delivery 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 Incremental Delivery 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 Incremental Delivery 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.

Incremental Delivery

Never "write everything, then test everything" — that strategy converts N independent small bugs into one entangled debugging session at the end. Small verified steps beat large unverified strides even when the stride looks 20% faster; something always goes wrong.

Step 1: Order the units

  1. Dependency order: shared types/schemas → pure logic → I/O adapters → interfaces (API/CLI/UI) → integration glue. Consumers are written after the things they consume exist and are verified.
  2. Risk first within that order: the unfamiliar API, the format never parsed, the algorithm you're unsure of — prove it in isolation before the plan depends on it. If it fails, the plan changes while changing is cheap.
  3. Pattern before replication: building N similar things? Build ONE end-to-end, verify it, THEN replicate. A flaw in the pattern costs 1× to fix before replication and N× after.

Step 2: Checkpoint after every unit

  1. After each coherent unit, run the fastest available check: compile, import, unit test, or a smoke run. Never stack a second untested layer on a first.
  2. In a git repo, commit (or otherwise mark) each known-good point so revert has a target.
  3. If a unit balloons mid-way: split it, checkpoint the finished half. Do not power through to a distant checkpoint.

Step 3: Maintain the state note

On any task long enough to survive a session break (or that another model might resume), keep a running note — a scratch file or persistent todo — containing:

  • Units done / units remaining (the ordered list, checked off).
  • Decisions later units must honor: naming scheme, error envelope shape, ID types, field formats — recorded the moment they're made.
  • Open questions and assumptions so far.
  • The exact next action, written for a cold reader.

Consult the note BEFORE starting each new unit — your memory of a decision made 8 units ago has drifted; the note hasn't. Write it as if a different, colder model will resume from it, because one might.

Step 4: Propagate interface changes immediately

When unit 7 forces a change to unit 2's interface (renamed field, new parameter, changed return shape):

  1. Grep for EVERY consumer of the old interface — do not fix only the ones you remember.
  2. Propagate the change to all of them NOW, not "after finishing unit 7".
  3. Re-run the earlier units' checks. Deferred propagation is how half-renamed codebases happen.

Step 5: Final consistency sweep

After the last unit, one deliberate pass over the WHOLE deliverable:

  • Same naming conventions from first file to last? (Early files follow the original plan; late files follow how the plan evolved — reconcile them.)
  • Same error format / response envelope / logging shape everywhere?
  • Do early sections still accurately describe what later sections actually do? Documents drift exactly like code.
  • Re-read the ORIGINAL request one final time and check every stated requirement and process instruction against the finished whole — long tasks decay instructions silently.

Checklist per unit

  • Consulted the state note before starting?
  • Unit's own check run and passing?
  • Any new cross-cutting decision recorded in the note?
  • Any interface change propagated to ALL existing consumers and their checks re-run?
  • State note updated (done list, next action)?

Worked example

Task: build a CLI tool with 5 subcommands reading a config, calling an API, and writing reports.

  • Order: (1) config loader + types [everything consumes it], (2) API client for ONE endpoint — riskiest, unfamiliar API — proven with a live smoke call, (3) first subcommand end-to-end [the pattern], (4–6) remaining subcommands [replication], (7) shared --help/error polish.
  • Checkpoint: after unit 2, the smoke call reveals the API paginates — recorded in the note: "all list endpoints must paginate; client returns iterators, not lists." Units 3–6 honor it; without the note, subcommand 5 (written hours later) would have returned truncated lists.
  • Unit 4 renames a config field for clarity → grep finds 3 consumers (units 1, 3, and a test); all updated in the same edit session; unit-1 and unit-3 tests re-run green.
  • Sweep: subcommands 3 and 6 formatted errors differently (evolved mid-task) — reconciled; original request re-read; requirement "machine-readable --json output" found present in units 3–5 but missing in 6 — fixed before delivery instead of shipped missing.

Done when

Every unit has passed its own check at its own checkpoint; all interface changes were propagated the moment they happened; the final consistency sweep is complete; the finished whole has been re-checked against the original request line by line; and the state note's "remaining" list is empty or every leftover item is explicitly reported as deferred.

Frequently asked questions

What does the Incremental Delivery AI skill do?

Build large multi-part deliverables — multi-file projects, migrations, long documents, N-of-the-same-thing work — in verified increments with dependency ordering, checkpoints, a running state note, and consistency sweeps. Trigger this when a task will produce 3+ files, spans more sub-deliverables than fit in one pass, involves repeating a pattern across many sites (endpoints, parsers, pages, records), or is explicitly requested to be done incrementally or resumably. Do NOT trigger for single-file changes (implementation-standards suffices) or for the initial breakdown of an ambiguous reques...

Why use Incremental Delivery on TypingMind?

Because you install it once and use it with any model. Incremental Delivery 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 Incremental Delivery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/UnpaidAttention/fable5-methodology/tree/main/skills/incremental-delivery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Incremental Delivery?

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 Incremental Delivery?

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

Is the Incremental Delivery AI skill free?

It is published on GitHub by UnpaidAttention. 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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