oma-pm
Turn product requirements into scoped tasks with dependencies and
Install / Use
npx skills add first-fluke/oh-my-agent --skill oma-pmInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Product ManagementSupported Platforms
Our assessment of oma-pm
oma-pm scores 87/100 on our quality scale, 46th of 79 Product Management skills we index.
Its SKILL.md is 7.4 KB long, well organised into 26 sections with 2 code examples: a thorough specification that gives an agent plenty to work with.
With 1,324 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 11 days ago, so oma-pm is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
oma-pm compared with similar skills
All 4 of these similar skills score higher than oma-pm; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| oma-pm (this skill)by first-fluke | 87 | 1.3k | 11d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 91.2k | 19d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.4k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 85.7k | today | MCP Server |
| crawl4aiby unclecode | 100 | 84.8k | today | MCP Server |
Frequently asked questions
- How do I install oma-pm?
- Run
npx skills add first-fluke/oh-my-agent --skill oma-pm. The install tabs above show the steps for each supported agent. - Which AI agents does oma-pm work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is oma-pm safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is oma-pm still maintained?
- The repository was last updated 11 days ago, so oma-pm is actively maintained.
Skill content
View source on GitHubname: oma-pm description: Turn product requirements into scoped tasks with dependencies and acceptance criteria. Use for implementation planning and prioritization.
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/
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
- Clarify the product goal, constraints, and target deliverables.
- Identify technical domains and required contracts.
- Decide whether ISO/risk/governance framing is relevant.
Scenes
- PREPARE: Gather requirements, constraints, and context.
- REASON: Decompose work, identify dependencies, risks, and API/data contracts.
- ACT: Produce JSON plan and task-board-compatible output.
- VERIFY: Check task atomicity, acceptance criteria, security/testing coverage, and dependency shape.
- 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
| Action | SSL primitive | Evidence |
|--------|---------------|----------|
| Read requirements/context | READ | User request and project context |
| Select planning structure | SELECT | Task template and workflow needs |
| Infer tasks and dependencies | INFER | Domain decomposition |
| Validate acceptance criteria | VALIDATE | Checklist and task schema |
| Write plan artifacts | WRITE | JSON plan and task-board markdown |
| Notify plan summary | NOTIFY | Final planning report |
Tools and instruments
- Task template, examples, ISO planning guide, shared API contracts
- Local filesystem for result artifacts
Canonical workflow path
1. Define API/data contracts.
2. Decompose tasks with agent, title, priority, dependencies, and acceptance criteria.
3. Save `.agents/results/plan-{sessionId}.json` using the injected session ID. Write the run-scoped report from the shared memory/result contract.
Resource scope
| Scope | Resource target |
|-------|-----------------|
| MEMORY | Requirements, assumptions, dependencies |
| LOCAL_FS | .agents/results/plan-{sessionId}.json, injected claim path and task/run-scoped report |
| CODEBASE | Optional 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
- For changed interfaces, reuse or define API/data contracts before dependent implementation tasks
- Every executable task has: agent, title,
{id, description}acceptance criteria, coveringrequired_checkswith exact argv/cwd, a replayabletaskprompt, priority tier (1 = independent, lower runs first), dependencies, and scope - Minimize dependencies for maximum parallel execution
- Security and testing are part of every task (not separate phases)
- Tasks should be completable by a single agent
- Output JSON plan + task-board.md for orchestrator compatibility
- 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) -
Runtime identity and run-scoped reports:
../_shared/runtime/memory-protocol.md,../_shared/runtime/result-contract.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
/planworkflow, also generatedocs/plans/work/{NNN}-{name}.mdper.agents/workflows/plan.md -
API contract template (SSOT):
../_shared/core/api-contracts/template.md; write generated contracts to.agents/results/api-contracts/(run artifact) ordocs/plans/contracts/(durable spec) -
Context loading:
../_shared/core/context-loading.md -
Planning depth:
../_shared/core/difficulty-guide.md(unresolved scope or dependencies) -
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)
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
