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Deepwork

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akitaonrails
deepwork

High-cost orchestrator workflow for large, high-risk, multi-phase coding efforts with meaningful dependencies and review gates. Do not activate for routine multi-file changes.

Overview

Publisherakitaonrails
Repositorymy-skills
Skill namedeepwork
Stars
268
Forks
31
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 akitaonrails on GitHub. Read the source before you install it.

Installation

Install the Deepwork 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/akitaonrails/my-skills/tree/master/deepwork
  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/akitaonrails/my-skills.git /tmp/my-skills
mkdir -p .claude/skills
cp -r /tmp/my-skills/deepwork .claude/skills/deepwork
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deepwork 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 Deepwork 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 Deepwork 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.

Deepwork

Deepwork is an orchestrator workflow for heavy coding sessions. Use it only when the work is clearly large or high-risk: multiple dependent phases, cross-cutting architectural change, unsafe-to-partially-ship migration, or sustained coordination across several specialist lanes.

Do not infer Deepwork merely because a task touches multiple files. Do not use it for trivial edits, quick docs changes, simple bug fixes, or routine bounded features.

Core Contract

When deepwork is active, the orchestrator must manage the work as a scheduler, not as the default implementation worker.

Setup and Deepwork State

  • create and maintain a local markdown progress file under .slim/deepwork/;
  • save code/doc deliverables to project paths (e.g. src/, docs/); reserve .slim/deepwork/ strictly for progress files;

Deepwork File

Create a task-specific file such as:

text
.slim/deepwork/<short-task-slug>.md

Before creating this file—and before planning or delegation—inspect the existing .gitignore and .ignore. Add only missing entries and do not add duplicates:

gitignore
# .gitignore
.slim/deepwork/
gitignore
# .ignore
!.slim/deepwork/
!.slim/deepwork/**

These rules keep deepwork state git-local while allowing OpenCode to read it.

Do not follow a rigid template. Choose whatever markdown structure best fits the work. The file only needs to remain useful as persistent session state and should capture, as applicable:

  • current goal and understanding;
  • researched, factual context from @librarian to avoid oracle doing its own research;
  • plan drafts, Oracle review budget/gates, and review notes;
  • implementation phases and status;
  • validation results;
  • unresolved questions, blockers, and follow-ups.

Update this file after major decisions, accepted research, reviews, phase completions, validation results, and scope changes. Record accepted findings and reference local files by path rather than copying their contents.

Planning

  • draft a plan before implementation;
  • create a phased implementation/delegation plan;
  • before dispatch, choose a small number of coherent implementation phases from the work's dependencies and natural delivery boundaries; do not split work merely to reduce an Oracle review's scope;
  • before execution, define coherent delivery phases and make an @oracle review mandatory after each one; record the phase order, specialist ownership, gate order, and one-line gate rationale in the deepwork file; share a compact version with the user;
  • before starting each phase, replace the OpenCode todo list with actionable delivery todos for that phase only;

Phase Execution

  • before each implementation phase, decide the execution path: what can run in parallel, what must be sequential, which specialists to delegate to, and whether to split the same agent into multiple bounded lanes;

Scheduler Discipline

Use the scheduler model throughout:

  • record task/session IDs and ownership boundaries;
  • wait for hook-driven background completion before consuming background results;
  • avoid blocking Orchestrator lane while background jobs run; if no independent work remains, stop briefly and let the completion event resume the workflow;
  • do not advance to the next phase while relevant jobs are running or terminal results are unreconciled.

Phase Gate and Commit

  • after each planned phase, run relevant validation, update the deepwork file, then request its planned @oracle gate before continuing;
  • before its planned Oracle gate, record relevant accepted research and file references so Oracle reviews established context rather than repeating discovery;
  • record the phase goal, changed paths, validation evidence, and the specific decision or risk to review in the deepwork file; provide this context to Oracle with the accepted research and file references;
  • when the phase changes module boundaries, dependency direction, or file placement, run an @explorer structure scan in parallel with the Oracle gate;
  • reconcile review findings, perform one bounded remediation pass for material issues, and validate that pass with focused evidence;
  • create a focused commit when the phase is an independently valid delivery boundary before starting the next phase;

Oracle Re-Reviews

Every planned Oracle gate has one initial review and may have at most two re-reviews. Request a re-review only when the remediation materially changes the reviewed decision or risk, or when the original concern cannot be verified with focused evidence. Do not spend a re-review on a mechanical or already-verified change.

State the attempt in every Oracle prompt, for example:

text
Gate 2 — review attempt 2 of 3 (1 re-review remaining)

For re-reviews, tell Oracle to prioritize unresolved material findings, risks introduced by remediation, and whether prior findings are resolved. It must not reopen accepted, unchanged, or resolved concerns. When the two re-reviews are exhausted, record any remaining material risk or blocker in the deepwork file and ask the user whether to accept the risk, change scope, or authorize an exceptional additional review.

Designer Handoff Guardrail

When a deepwork phase includes @designer, treat the delivered UI/UX as accepted design intent for later phases. Record any important design decisions in the deepwork file before continuing.

After designer work:

  • preserve layout, rhythm, hierarchy, motion, spacing, color, affordances, responsiveness, and component feel;
  • review and improve user-facing copy with grounded, normal wording, but do not change visual structure or interaction intent;
  • route follow-up visual, responsive, motion, hierarchy, polish, or component-feel changes back to @designer;
  • use @fixer only for bounded mechanical follow-up that preserves the design exactly, such as wiring, tests, type fixes, or non-visual behavior changes;
  • if design intent must change, record why in the deepwork file before changing it.

Completion

  • finish with final validation and a concise summary.

Frequently asked questions

What does the Deepwork AI skill do?

High-cost orchestrator workflow for large, high-risk, multi-phase coding efforts with meaningful dependencies and review gates. Do not activate for routine multi-file changes.

Why use Deepwork on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/akitaonrails/my-skills/tree/master/deepwork. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deepwork?

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 Deepwork?

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

Is the Deepwork AI skill free?

It is published on GitHub by akitaonrails. 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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