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Agency Os

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jeremylongshore
agency-os

Notion-as-source-of-truth dispatch board for running your work like an AI agency. One Tasks database is the source of truth; tasks flow Suggestion through Discussion, To-Do, In Progress, and Done with subtasks, recurring cadences, dependencies, and template subtrees. Batch execution fans approved To-Do rows out to parallel agents with per-task model selection. Use when capturing chat to Notion, running the To-Do queue, suggesting, approving, or discussing tasks, or coordinating multi-task batches. Trigger with "/agency-os" subcommands or natural-language variants ("add a suggestion: …", "let's discuss X", "run the queue").

Overview

Publisherjeremylongshore
Repositorytons-of-skills-marketplace
Skill nameagency-os
Stars
2.8K
Forks
402
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

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

Installation

Install the Agency Os 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.
  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/jeremylongshore/tons-of-skills-marketplace.git /tmp/tons-of-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/tons-of-skills-marketplace/plugins/ai-agency/agency-os/skills/agency-os .claude/skills/agency-os
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agency Os 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 Agency Os 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 Agency Os 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.

agency-os

Notion-as-source-of-truth dispatch board. One Tasks database, one Hub page, one page per Corpus, one page each for General Guidance and Resources. The skill mutates Notion via the Notion MCP (mcp__*__notion-* tools); only references/notion-pointers.json is committed to git.

Skill name decision: the skill is named agency-os (matching the repo). All commands are /agency-os <cmd>. This is the single plugin entry point; there is no agency-os/notion sub-namespace. If you embed this plugin alongside others, prefix commands with agency-os to avoid collisions.

Overview

agency-os turns a single Notion database into a multi-status dispatch board for AI work. The model is intentionally narrow:

  • One Tasks database is the source of truth for status, priority, model selection, and ownership. No parallel kanban tools.
  • One Hub page holds the General Guidance, Resources, and Corpus pointers that every task consults.
  • Tasks flow through five statuses: Suggestion → Discussion → To-Do → In Progress → Done. The dedup gate at each transition prevents accidental re-execution.
  • run fans approved To-Do rows out to parallel agents. Each task carries its own model selection (Haiku for cheap fan-out, Sonnet for default, Opus for hard reasoning) and respects declared dependencies.

The skill is stateless on disk — the only committed artifact is references/notion-pointers.json (database/page IDs). All runtime state lives in Notion.

For the full architecture (status flow, sync protocol, workspace structure, pointer/cache format), see references/architecture.md.

Prerequisites

  • Notion MCP server installed: npx -y @notionhq/notion-mcp-server (declare in .mcp.json)
  • Notion integration token (NOTION_TOKEN) with read+write access to your workspace. Add .env to .gitignore — never commit the token.
  • A Notion Tasks database with the columns the skill expects (see references/architecture.md § "Workspace structure" for the schema). The /agency-os init command can scaffold this for you.
  • Python 3 for the optional scripts/query-tasks.py helper.

First-time setup:

bash
ccpi install agency-os
# then in Claude Code:
/agency-os init --harness=basic --haiku=cost-tier --sonnet=default --opus=hard-reasoning

Instructions

The skill is a CLI surface over Notion. Three usage patterns:

  1. Direct command invocation/agency-os <cmd> [args]. See references/commands.md for the full reference of 19 commands (init, scaffold, suggest, discuss, log, add-subtask, approve, start, refresh, run, done, kill, next, status, list, show, update, move, plus launch alias).

  2. Natural-language driving — the skill translates conversational chat into the corresponding command. Examples in references/natural-language.md.

  3. Batch execution/agency-os run [--go] fans the entire To-Do queue out to parallel agents with per-task model selection. See ## Examples below for the canonical flow.

Status flow is enforced — you cannot skip a stage. Every command performs a sync preflight to ensure your local view of Notion is current (see references/architecture.md § "Sync — preflight on every command").

When drafting any user-facing copy (READMEs, blog posts, launch surfaces), apply the positioning brief at references/positioning.md before writing.

Output

Every command returns to chat with:

  • Verdict line✅ <action> or ⚠️ <reason> (one line, scannable)
  • Affected task IDs and titles — every task touched, with its new status
  • Next-action hint — what command the operator would typically run next

Batch run additionally emits:

  • A per-task pass/fail table
  • Total model spend estimate (Haiku/Sonnet/Opus call counts)
  • Outstanding-dependency callouts for tasks that couldn't start

Error Handling

The skill fails closed on five well-defined cases (full details in references/architecture.md § "Status flow — the dedup gate"):

ConditionBehavior
Notion API auth failsHalt, print "NOTION_TOKEN missing or invalid", exit 1
Database/page ID drift (pointers stale)Halt, print "Run /agency-os refresh", exit 1
Status-flow violation (e.g. approve on a Suggestion)Halt with the required prerequisite step quoted
Dependency cycle detected during runHalt, list the cycle, exit 1
Task missing required model selectionHalt, print "Run /agency-os update <id> --model <tier>"

The skill never silently corrects state in Notion — every fix is an explicit command the operator must run.

Examples

Capture a chat insight as a Suggestion:

text
User: add a suggestion: refactor the auth flow to use the new token cache
Skill: → /agency-os suggest "refactor the auth flow to use the new token cache"
       ✅ Created Suggestion #t-2026-05-23-001 in corpus "platform"
       Next: /agency-os discuss t-2026-05-23-001

Approve and run a batch:

text
User: approve t-2026-05-23-{001..003} then run the queue
Skill: ✅ Approved 3 tasks → To-Do
       /agency-os run --go
       → fanning to 3 parallel agents...
       ✅ Done: 2 | ⚠️ Blocked on deps: 1 | Total spend: ~$0.04

More examples and the full command catalog are in references/commands.md.

Resources

  • Plugin source: https://github.com/ratamaha-git/agency-os
  • Launch post: https://automatelab.tech/agency-os-launch/
  • references/architecture.md — status flow, sync protocol, workspace schema
  • references/commands.md — full CLI reference (19 commands)
  • references/natural-language.md — chat-to-command translation table
  • references/positioning.md — canonical brief for user-facing copy
  • references/general-guidance.md — shared operating principles applied to every task
  • references/notion-pointers.json — pointer file scaffold (database/page IDs)
  • references/task-page-template.md — Notion page template for new tasks
  • references/corpus-template.md — Notion page template for a Corpus
  • references/config-template.json — default per-task model routing
  • scripts/query-tasks.py — optional Python helper for offline introspection

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 Agency Os AI skill do?

Notion-as-source-of-truth dispatch board for running your work like an AI agency. One Tasks database is the source of truth; tasks flow Suggestion through Discussion, To-Do, In Progress, and Done with subtasks, recurring cadences, dependencies, and template subtrees. Batch execution fans approved To-Do rows out to parallel agents with per-task model selection. Use when capturing chat to Notion, running the To-Do queue, suggesting, approving, or discussing tasks, or coordinating multi-task batches. Trigger with "/agency-os" subcommands or natural-language variants ("add a suggestion: …", "le...

Why use Agency Os on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/agency-os/skills/agency-os. 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 Agency Os?

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 Agency Os?

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

Is the Agency Os AI skill free?

Yes. It is published on GitHub by jeremylongshore 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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