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Publish

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Q00
publish

Publish Seed specification as GitHub Issues for team-based project management

Overview

PublisherQ00
Repositoryouroboros
Skill namepublish
Stars
6K
Forks
605
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Q00 on GitHub. Read the source before you install it.

Installation

Install the Publish 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/Q00/ouroboros.git /tmp/ouroboros
mkdir -p .claude/skills
cp -r /tmp/ouroboros/skills/publish .claude/skills/publish
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Publish 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 Publish 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 Publish 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.

/ouroboros:publish

Convert a Seed specification into structured GitHub Issues for team workflows.

Usage

ooo publish [seed_path]
/ouroboros:publish [seed_path]

Trigger keywords: "publish to github", "create issues from seed", "seed to issues"

Instructions

When the user invokes this skill:

Step 1: Prerequisite Check

1a. Verify gh CLI is installed:

bash
command -v gh >/dev/null 2>&1 && echo "OK" || echo "MISSING"

If missing, tell the user:

GitHub CLI (gh) is not installed.
Install it: https://cli.github.com/

Stop.

1b. Verify gh is authenticated:

bash
gh auth status

If not authenticated, tell the user:

GitHub CLI is not authenticated. Run: gh auth login

Stop.

Step 2: Locate the Seed

Ouroboros stores seeds in ~/.ouroboros/seeds/:

  • Interview seeds: ~/.ouroboros/seeds/{seed_id}.yaml (YAML)
  • PM seeds: ~/.ouroboros/seeds/pm_seed_{id}.json (JSON)

Determine the Seed source in this priority order:

  1. Explicit path argument: If the user provided a file path (.yaml or .json), read it directly
  2. Most recent seed file: Search for the most recent seed in the standard location:
    bash
    ls -t ~/.ouroboros/seeds/*.yaml ~/.ouroboros/seeds/*.json 2>/dev/null | head -5
    If multiple seeds exist, present the top candidates via AskUserQuestion and let the user choose.
  3. Conversation context: If ooo seed or ooo pm was just run in this conversation and the seed path was reported, use that path.

If no seed is found:

No Seed found. Run `ooo seed` or `ooo pm` first to generate a specification.

Stop.

Step 3: Parse the Seed

Detect the file format by extension and parse accordingly:

For YAML seeds (from ooo interview + ooo seed): Read the YAML file and extract:

  • goal → Epic title and description
  • constraints → Listed in Epic body
  • acceptance_criteria → Checklist items in Epic + distributed to Task issues
  • ontology_schema → Documentation section in Epic
  • evaluation_principles → Quality criteria reference
  • exit_conditions → Definition of Done
  • metadata.ambiguity_score → Confidence indicator
  • metadata.seed_id → Used for duplicate detection

For JSON seeds (from ooo pm): Read the JSON file and extract fields using the actual PMSeed schema:

PMSeed fieldMaps to
pm_idSeed identifier (for duplicate detection)
product_nameEpic title prefix
goalEpic Goal section
constraintsEpic Constraints section (array of strings)
success_criteriaAcceptance Criteria checklist (array of strings)
user_storiesUser Stories section (array of {persona, action, benefit})
deferred_itemsDeferred Items section (array of strings)
decide_later_itemsOpen Questions section (array of strings)
assumptionsAssumptions section (array of strings)

Format user stories as: "As a {persona}, I want to {action}, so that {benefit}."

If any field is missing or empty, omit that section from the Epic body rather than failing.

Step 4: Detect Repository

4a. Attempt auto-detection from current directory:

bash
gh repo view --json nameWithOwner -q '.nameWithOwner' 2>/dev/null

4b. Present the target repo choice via AskUserQuestion:

If auto-detection succeeded:

json
{
  "questions": [{
    "question": "Publish Seed as GitHub Issues to this repository?",
    "header": "Target Repository",
    "options": [
      {"label": "<detected_repo>", "description": "Use current repository"},
      {"label": "Other", "description": "I'll specify a different owner/repo"}
    ],
    "multiSelect": false
  }]
}

If auto-detection failed (not in a git repo):

json
{
  "questions": [{
    "question": "Which GitHub repository should the issues be created in? (format: owner/repo)",
    "header": "Target Repository"
  }]
}

If the user chose "Other", ask:

json
{
  "questions": [{
    "question": "Enter the target repository (format: owner/repo):",
    "header": "Target Repository"
  }]
}

Store the resolved repository as TARGET_REPO. All subsequent gh commands MUST include -R <TARGET_REPO> to ensure they target the correct repository.

Step 5: Duplicate Check

Before creating issues, check if this seed was already published:

bash
gh issue list -R <TARGET_REPO> --label "ouroboros" --state all --search "<seed_id or pm_id>" --limit 5 --json number,title,state

The search uses the seed's unique identifier (metadata.seed_id for YAML seeds, pm_id for JSON seeds). This works because Step 7 persists the identifier in the Epic body (see the Seed ID field in the Epic template).

If matching issues are found, warn the user via AskUserQuestion:

json
{
  "questions": [{
    "question": "Found existing Ouroboros issues that may be from the same seed:\n\n<list of matching issues>\n\nCreate new issues anyway?",
    "header": "Duplicate Warning",
    "options": [
      {"label": "Create anyway", "description": "Proceed with new issues"},
      {"label": "Cancel", "description": "Do not create duplicate issues"}
    ],
    "multiSelect": false
  }]
}

If "Cancel": Stop.

Step 6: Plan Issue Structure

Before creating issues, present the planned structure to the user for review.

6a. Break down acceptance criteria into Task groups:

Analyze the acceptance criteria and group them into logical implementation units. Each unit becomes a Task issue. Use your understanding of the domain to create meaningful groupings (e.g., group by feature area, layer, or dependency order).

6b. Present the plan via AskUserQuestion:

json
{
  "questions": [{
    "question": "Here's the planned issue structure:\n\n**Epic**: <goal summary>\n\n**Tasks**:\n1. <task_1_title> — <brief scope>\n2. <task_2_title> — <brief scope>\n3. <task_3_title> — <brief scope>\n\nProceed with creating these issues?",
    "header": "Issue Plan",
    "options": [
      {"label": "Create issues", "description": "Publish to GitHub now"},
      {"label": "Modify plan", "description": "I want to adjust the structure first"}
    ],
    "multiSelect": false
  }]
}

If "Modify plan": Ask what to change, adjust, and re-present.

Step 7: Create GitHub Issues

IMPORTANT: Every gh command in this step MUST include -R <TARGET_REPO>.

Issue number extraction: gh issue create outputs a URL like https://github.com/owner/repo/issues/42. Extract the issue number by parsing the trailing digits:

bash
EPIC_URL=$(gh issue create -R <TARGET_REPO> --title "..." --label "..." --body "...")
EPIC_NUM=$(echo "$EPIC_URL" | grep -o '[0-9]*$')

Apply the same extraction pattern for every Task issue created.

7a. Create labels (if they don't exist):

bash
gh label create "ouroboros" -R <TARGET_REPO> --description "Created by Ouroboros publish" --color "6f42c1" 2>/dev/null || true
gh label create "epic" -R <TARGET_REPO> --description "Epic / parent issue" --color "0075ca" 2>/dev/null || true
gh label create "task" -R <TARGET_REPO> --description "Implementation task" --color "008672" 2>/dev/null || true

7b. Create the Epic issue:

bash
gh issue create -R <TARGET_REPO> \
  --title "[Epic] <goal_summary>" \
  --label "ouroboros,epic" \
  --body "$(cat <<'BODY'
## Goal

<goal from seed>

## Constraints

<constraints as bullet list>

## Acceptance Criteria

- [ ] <criterion_1>
- [ ] <criterion_2>
- ...

## Ontology

| Field | Type | Description |
|-------|------|-------------|
| <field_name> | <type> | <description> |

## Evaluation Principles

| Principle | Weight | Description |
|-----------|--------|-------------|
| <name> | <weight> | <description> |

## Exit Conditions

<exit conditions as bullet list>

---

**Seed ID**: `<seed_id or pm_id>` | **Ambiguity Score**: <score> | **Seed**: `<seed_file_path>`
*Generated by [Ouroboros](https://github.com/Q00/ouroboros) via `ooo publish`*
BODY
)"

Capture the Epic issue number from the output.

7c. Create Task issues (one per implementation unit):

For each task:

bash
gh issue create -R <TARGET_REPO> \
  --title "[Task] <task_title>" \
  --label "ouroboros,task" \
  --body "$(cat <<'BODY'
Parent: #<epic_number>

## Scope

<what this task covers>

## Acceptance Criteria

- [ ] <specific_criterion_1>
- [ ] <specific_criterion_2>

## Test Checklist

- [ ] <test_1>
- [ ] <test_2>
- [ ] <test_3>

## Pass Criteria

<measurable conditions for this task to be considered done>

---

*Part of [Epic] #<epic_number> | Generated by [Ouroboros](https://github.com/Q00/ouroboros) via `ooo publish`*
BODY
)"

7d. Update Epic with task links:

After all tasks are created, add a comment to the Epic:

bash
gh issue comment <epic_number> -R <TARGET_REPO> --body "$(cat <<'BODY'
## Implementation Tasks

- [ ] #<task_1_number> — <task_1_title>
- [ ] #<task_2_number> — <task_2_title>
- [ ] #<task_3_number> — <task_3_title>

Track overall progress by checking off tasks as their issues are closed.
BODY
)"

Step 8: Summary

Present the results:

Published to <TARGET_REPO>:

  #<epic>  [Epic] <goal_summary>
    ├── #<task_1>  [Task] <task_1_title>
    ├── #<task_2>  [Task] <task_2_title>
    └── #<task_3>  [Task] <task_3_title>

View: https://github.com/<TARGET_REPO>/issues/<epic>

Then suggest next steps:

Next steps:
  - Assign tasks to team members on GitHub
  - Use GitHub Projects board for tracking
  - Run `ooo run` for AI-assisted implementation of individual tasks

Notes

  • No MCP required: This skill works entirely through gh CLI
  • Non-destructive: Creates new issues only, never modifies existing ones
  • Cross-repo support: All gh commands use -R <TARGET_REPO>, so seeds can be published to any repository the user has write access to
  • Works with both seed formats: YAML seeds from ooo seed and JSON seeds from ooo pm are both supported — the parser auto-detects format by file extension

RFC #1392 State Breadcrumb Footer

Your final response MUST end with exactly one breadcrumb footer line:

◆ <current state> → next: <recommended action>

Derive <current state> from live session state via ouroboros_session_status when that MCP projection is available; otherwise derive it from this skill's actual outcome. Never use a linear Step N of M footer because Ouroboros is an evolutionary loop. When the next action is genuinely a choice, list 2-3 honest options in the next: clause. The breadcrumb line must be the last line of the response.

Frequently asked questions

What does the Publish AI skill do?

Publish Seed specification as GitHub Issues for team-based project management

Why use Publish on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Q00/ouroboros/tree/main/skills/publish. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Publish?

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 Publish?

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

Is the Publish AI skill free?

Yes. It is published on GitHub by Q00 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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