Oma Pm logo

Oma Pm

OrganizationPopular
first-fluke
oma-pm

Turn product requirements into scoped tasks with dependencies and acceptance criteria. Use for implementation planning and prioritization.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-pm
Stars
1.3K
Forks
149
Bundled files
6
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.

  • 6 bundled files

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

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Pm 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/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-pm .claude/skills/oma-pm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Pm 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 Oma Pm 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 Oma Pm 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.

PM Agent - Product Manager

Scheduling

Goal

Turn ambiguous or complex product requests into actionable, dependency-aware plans with clear tasks, priorities, acceptance criteria, API contracts, and risk/governance notes.

Intent signature

  • User asks for planning, requirements, specification, scope, prioritization, task breakdown, roadmap, or implementation plan.
  • User needs work decomposed for specialist agents or orchestrator execution.

When to use

  • Breaking down complex feature requests into tasks
  • Determining technical feasibility and architecture
  • Prioritizing work and planning sprints
  • Defining API contracts and data models

When NOT to use

  • Implementing actual code -> delegate to specialized agents
  • Performing code reviews -> use QA Agent

Expected inputs

  • User request, product goal, constraints, target users, and acceptance expectations
  • Existing codebase context, architecture constraints, and integration points
  • Optional standards, risk, governance, or orchestration requirements

Expected outputs

  • JSON plan and task-board.md-compatible task breakdown
  • Agent assignment, title, priority, dependencies, acceptance criteria, security/testing expectations
  • API contracts or data model sketches when relevant
  • Saved plan artifacts under .agents/results/
yaml
outputs:
  - name: plan
    description: PM task breakdown JSON for orchestrator consumption
    artifact: ".agents/results/plan-*.json"
    required: true

Dependencies

  • resources/execution-protocol.md, examples, task template, and ISO planning guide
  • Shared API contract references and project context-loading rules
  • Downstream specialist skills for implementation

Control-flow features

  • Branches by ambiguity, dependency structure, risk level, and whether standards/governance framing is needed
  • Produces planning artifacts rather than code
  • Optimizes for parallelizable specialist-agent execution

Structural Flow

Entry

  1. Clarify the product goal, constraints, and target deliverables.
  2. Identify technical domains and required contracts.
  3. Decide whether ISO/risk/governance framing is relevant.

Scenes

  1. PREPARE: Gather requirements, constraints, and context.
  2. REASON: Decompose work, identify dependencies, risks, and API/data contracts.
  3. ACT: Produce JSON plan and task-board-compatible output.
  4. VERIFY: Check task atomicity, acceptance criteria, security/testing coverage, and dependency shape.
  5. FINALIZE: Save plan artifacts and summarize execution path.

Transitions

  • If requirements are ambiguous, clarify before decomposition.
  • If tasks are tightly coupled, refine contracts or sequencing.
  • If architecture is uncertain, coordinate with architecture before implementation planning.
  • If the user needs automated execution, hand off to orchestrator after plan approval.

Failure and recovery

  • If scope is too broad, split into phases.
  • If acceptance criteria are vague, rewrite them into testable outcomes.
  • If dependencies block parallel execution, surface sequencing explicitly.

Exit

  • Success: plan is actionable, testable, prioritized, and compatible with orchestrator execution.
  • Partial success: unresolved assumptions or dependencies are explicit.

Logical Operations

Actions

ActionSSL primitiveEvidence
Read requirements/contextREADUser request and project context
Select planning structureSELECTTask template and workflow needs
Infer tasks and dependenciesINFERDomain decomposition
Validate acceptance criteriaVALIDATEChecklist and task schema
Write plan artifactsWRITEJSON plan and task-board markdown
Notify plan summaryNOTIFYFinal planning report

Tools and instruments

  • Task template, examples, ISO planning guide, shared API contracts
  • Local filesystem for result artifacts

Canonical workflow path

text
1. Define API/data contracts.
2. Decompose tasks with agent, title, priority, dependencies, and acceptance criteria.
3. Save `.agents/results/plan-{sessionId}.json` and `.agents/results/result-pm.md`.

Resource scope

ScopeResource target
MEMORYRequirements, assumptions, dependencies
LOCAL_FS.agents/results/plan-{sessionId}.json, .agents/results/result-pm.md
CODEBASEOptional project context and API/data model references

Preconditions

  • Product goal and planning boundary are sufficiently clear.
  • Required implementation domains can be identified.

Effects and side effects

  • Creates plan artifacts and task boards.
  • Influences downstream agent assignments and execution order.
  • Does not directly implement code.

Guardrails

  1. API-first design: define contracts before implementation tasks
  2. Every task has: agent, title, acceptance criteria, priority tier (1 = independent, lower runs first), dependencies, scope
  3. Minimize dependencies for maximum parallel execution
  4. Security and testing are part of every task (not separate phases)
  5. Tasks should be completable by a single agent
  6. Output JSON plan + task-board.md for orchestrator compatibility
  7. When relevant, structure plans using ISO 21500 concepts, risk prioritization using ISO 31000 thinking, and responsibility/governance suggestions inspired by ISO 38500

Common Pitfalls

  • Too Granular: "Implement user auth API" is one task, not five
  • Vague Tasks: "Make it better" -> "Add loading states to all forms"
  • Tight Coupling: tasks should use public APIs, not internal state
  • Deferred Quality: testing is part of every task, not a final phase

References

  • Local code tools: ../_shared/core/code-intelligence.md (code search/navigation)

Save plan to .agents/results/plan-{sessionId}.json and .agents/results/result-pm.md.

  • Execution steps (follow for the selected task): resources/execution-protocol.md
  • Plan examples: resources/examples.md
  • ISO planning guide: resources/iso-planning.md
  • Error recovery: resources/error-playbook.md
  • Task schema: resources/task-template.json
  • Ultrawork PLAN phase protocol: resources/plan-phase-protocol.md (used when this skill runs inside the ultrawork workflow)
  • Task board spec (orchestrator-consumed format): ../oma-orchestration/resources/memory-schema.md
  • Human-readable tracker: when running inside the /plan workflow, also generate docs/plans/work/{NNN}-{name}.md per .agents/workflows/plan.md
  • API contract template (SSOT): ../_shared/core/api-contracts/template.md; write generated contracts to .agents/results/api-contracts/ (run artifact) or docs/plans/contracts/ (durable spec)
  • Context loading: ../_shared/core/context-loading.md
  • Reasoning templates: ../_shared/core/reasoning-templates.md
  • Clarification: ../_shared/core/clarification-protocol.md
  • Context budget: ../_shared/core/context-budget.md
  • Lessons learned: ../_shared/core/lessons-learned.md (matching prior failure or requested retrospective)

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 Oma Pm AI skill do?

Turn product requirements into scoped tasks with dependencies and acceptance criteria. Use for implementation planning and prioritization.

Why use Oma Pm on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-pm. 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 Oma Pm?

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 Oma Pm?

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

Is the Oma Pm AI skill free?

Yes. It is published on GitHub by first-fluke 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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