Octo Loop logo

Octo Loop

Organization
Mininglamp-OSS
octo-loop

Use when operating the Octo Loop control plane through the octo-cli `loop` commands: reading or writing Fleet tasks, comments, metadata, projects, and labels; dispatching work to experts or expert-teams and watching or cancelling their executions; creating, triaging, updating status, or reading progress on a task; listing tasks, experts, or expert-teams in a workspace; and safely handling mention and status-change side effects. Terminology: task (was issue), expert (was agent), expert-team (was squad), dispatch (assign a task to an expert/expert-team and trigger its run). Trigger phrases include: create or report a task/bug, dispatch or schedule work, assign to an expert, run or rerun a task, update or read progress, comment on or reply to a task, list or search tasks in a Loop workspace. Do not use for chit-chat unrelated to Loop, for pure local file work, or when a task or issue clearly refers to GitHub/Jira rather than Loop. Load after octo-shared.

Overview

PublisherMininglamp-OSS
Repositoryocto-cli
Skill nameocto-loop
Stars
918
Forks
130
Bundled files
Instructions only
LicenseApache-2.0
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 Mininglamp-OSS on GitHub. Read the source before you install it.

Installation

Install the Octo Loop 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/Mininglamp-OSS/octo-cli/tree/main/skills/octo-loop
  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/Mininglamp-OSS/octo-cli.git /tmp/octo-cli
mkdir -p .claude/skills
cp -r /tmp/octo-cli/skills/octo-loop .claude/skills/octo-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Octo Loop 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 Octo Loop 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 Octo Loop 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.

Octo Loop

Use the loop namespace for Fleet control-plane operations. Product names are task, expert, expert-team, and execution; do not use the legacy issue/agent/squad names.

Terminology (legacy → current): agent → expert, squad → expert-team, issue → task, assign a task to an expert/expert-team and trigger its run → dispatch.

When to use / not use

Use this skill when the request targets a specific Loop workspace or task: reading or writing tasks, comments, projects, or metadata; working with experts or expert-teams (dispatch, schedule, trigger a run); replying to a comment, creating or triaging a task, updating status, or reading progress.

Do not use it for chat unrelated to Loop, for pure local file work, or when "issue" clearly means GitHub/Jira rather than Loop.

Authentication

Set OCTO_BOT_TOKEN to a bearer credential Fleet accepts directly, such as bf_, uk_, or octo_loop_. The CLI does not infer a human, device, or execution principal from a token prefix. Fleet verifies the credential and constructs the principal.

Outside daemon task mode, if a command fails with an auth error, check octo-cli auth status and let the user run octo-cli auth login. In OCTO_CREDENTIAL_MODE=task, those profile diagnostics are unavailable: report the auth failure so the daemon can refresh or replace the injected credential. Do not fabricate credentials. Never leak or store tokens, config keys, or API base URLs, and do not bypass workspace permissions with direct HTTP calls.

All Loop commands use the same gateway as every other domain. The embedded paths include the Fleet module namespace, so requests resolve under $OCTO_API_BASE_URL/fleet/api/v1/*.

Workspace selector

Workspace-scoped Loop commands require the --workspace-id <id> flag (sent as the X-Workspace-ID header). There is no environment fallback for it — pass it explicitly. Discover available workspaces first:

bash
octo-cli loop workspace list
octo-cli loop workspace member list <workspace-id> --workspace-id <workspace-id>

Discover and execute operations

Output is JSON by default (--format json); parse JSON rather than scraping tables.

bash
octo-cli loop task list --workspace-id <ws>
octo-cli loop task get <task-id> --workspace-id <ws>
octo-cli loop expert list --workspace-id <ws>
octo-cli loop expert-team list --workspace-id <ws>
octo-cli loop execution message list <execution-id> --workspace-id <ws>

Inspect the current contract before constructing a write:

bash
octo-cli schema task.create
octo-cli schema task.comment.create
octo-cli schema expert.create
octo-cli schema expert_team.create

Repository operations, daemon device enrollment, task claim/heartbeat, and local runtime paths are intentionally not part of this CLI surface.

Read workflow

Read before you write.

bash
octo-cli loop task get <task-id> --workspace-id <ws>
octo-cli loop task comment list <task-id> --workspace-id <ws>
octo-cli loop task metadata list <task-id> --workspace-id <ws>
octo-cli loop task children-by-parent --workspace-id <ws>

Comment history is paginated with --page / --page-size.

Explore other namespaces (project, expert, expert-team, expert-template, execution, runtime, skill, skill-file, autopilot, attachment, label, top-level comment) with --help — their shapes vary.

Write workflow

Treat writes as actions with side effects: creating comments, creating tasks, changing status, assigning/dispatching, rerunning executions, mentioning an expert or expert-team, and autopilot changes. If the user has not clearly asked for a write, confirm before executing.

Comment and description bodies go through a file

There is no --content-file flag. Body text is a request-body field, so pass it via --data, which accepts @file (or @- for stdin). Do not build the body inline with --content "$(cat ...)": the shell rewrites backticks, $(), variables, quotes, and newlines before the CLI sees them.

Write the body to a private temp file, wrap it as valid JSON (use jq -Rs . to escape the text safely), and clean up:

bash
body_dir="$(mktemp -d)"
trap 'rm -rf "$body_dir"' EXIT
# ...write the comment body to "$body_dir/reply.md", preserving real newlines...
printf '{"content": %s}' "$(jq -Rs . < "$body_dir/reply.md")" > "$body_dir/body.json"

octo-cli loop task comment create <task-id> --workspace-id <ws> --data "@$body_dir/body.json"

Use mktemp -d, never a fixed path like ./reply.md (it can clobber a user file). To reply within a thread, add --parent-comment-id <comment-id>; individual flags override same-named fields in --data.

Task descriptions follow the same pattern — --description is a plain string flag with the same shell hazards, so prefer a JSON body from a file:

bash
printf '{"title": %s, "description": %s}' \
  "$(jq -Rs . <<< 'Fix login redirect')" \
  "$(jq -Rs . < "$body_dir/description.md")" > "$body_dir/task.json"
octo-cli loop task create --workspace-id <ws> --data "@$body_dir/task.json"

Metadata

Metadata is persistent task state, not a log. Read it on entry and write only high-value facts that will be read again (for example pr_url, pipeline_status, blocked_reason). Inspect the contract first — the set path is keyed by a metadata id:

bash
octo-cli schema task.metadata.set
octo-cli loop task metadata list <task-id> --workspace-id <ws>

Status and assignment side effects

Status and assignment are done through task update, not a dedicated command:

bash
octo-cli loop task update <task-id> --workspace-id <ws> --status <status>
octo-cli loop task update <task-id> --workspace-id <ws> \
  --assignee-id <uuid> --assignee-type expert   # or expert_team, member

Changing status is not cosmetic — it can start or stop work. In the standard Fleet workflow backlog parks an assigned task, in_progress means work is in progress, and in_review means the work is awaiting review. Use done only after the work is accepted or no review is required; done and cancelled are terminal. Moving out of backlog to an active status can trigger the assigned expert to run. Confirm the exact status values accepted by your workspace via the schema.

Dispatch and executions

To create a task and dispatch it to an expert or expert-team in one call, use quick-create with a prompt and an assignee id:

bash
octo-cli loop task quick-create --workspace-id <ws> \
  --prompt "Investigate the login redirect bug" --expert-id <uuid>
# or --expert-team-id <uuid>

Each dispatch produces an execution. Read run state and stop a runaway run rather than guessing:

bash
octo-cli loop task execution list <task-id> --workspace-id <ws>
octo-cli loop task execution cancel <task-id> <execution-id> --workspace-id <ws>
octo-cli loop task rerun <task-id> --workspace-id <ws> --execution-id <execution-id>

Cancelling and rerunning are writes with side effects — confirm before running them unless the user asked.

Mention side effects

Comment bodies can contain mention links that Fleet interprets as actions, not decoration. Mentioning an expert or expert-team triggers a run; mentioning a person is a notification only. Look up real ids before composing a mention:

bash
octo-cli loop expert list --workspace-id <ws>
octo-cli loop expert-team list --workspace-id <ws>
octo-cli loop workspace member list <ws> --workspace-id <ws>

Do not mention an expert or expert-team just to say thanks, confirm, or wrap up — re-mentioning the responder can trigger a fresh run and create a loop. Confirm the exact mention link syntax against the product before relying on it.

External agent boundary

An external agent does not automatically inherit Loop context. When asked to act on a task or comment, obtain or derive: the task id, the triggering comment id and its parent (if replying), the target workspace, whether writes are allowed, and whether mentions / status changes / reruns / dispatch are allowed. If any of these is missing and the operation would write state, ask first. For read-only investigation, gather context with JSON output and state what is still missing.

Frequently asked questions

What does the Octo Loop AI skill do?

Use when operating the Octo Loop control plane through the octo-cli `loop` commands: reading or writing Fleet tasks, comments, metadata, projects, and labels; dispatching work to experts or expert-teams and watching or cancelling their executions; creating, triaging, updating status, or reading progress on a task; listing tasks, experts, or expert-teams in a workspace; and safely handling mention and status-change side effects. Terminology: task (was issue), expert (was agent), expert-team (was squad), dispatch (assign a task to an expert/expert-team and trigger its run). Trigger phrases in...

Why use Octo Loop on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mininglamp-OSS/octo-cli/tree/main/skills/octo-loop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Octo Loop?

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 Octo Loop?

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

Is the Octo Loop AI skill free?

Yes. It is published on GitHub by Mininglamp-OSS under the Apache-2.0 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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