oh-my-agent is a harness for checking whether coding agents actually completed their work by verifying tests, required artifacts, independent reviews, and recorded decisions. It is used across multiple agent runtimes to make workflow results auditable instead of relying on an agent's own report. The catalogue add-ons provide parts of its skills, agents, hooks, MCP integrations, instructions, and plugins.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add first-fluke/oh-my-agent --skill oma-pmgit clone --depth 1 https://github.com/first-fluke/oh-my-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-pm)<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-pm"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-pm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-pm"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-pm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.01586 |
| Opus 5.5 | $0.00010 | $0.00634 |
| Sonnet 5.5 | $0.00005 | $0.00317 |
| Haiku 4.5 | $0.00002 | $0.00159 |
Grade A, and why
oma-pm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 20d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 20d ago Changed · +1 lines 68ae4c0fd7a8
- 1mo ago Changed · -3 lines · -31 tokens per session cbe2b3e00660
- 1mo ago Changed · -1 lines 6bb10cbabdbf
- 1mo ago Changed 538681954019
- 1mo ago First seen · 160 lines · 55 tokens per session scan A 4271a4796e7a
oma-pm is a skill published in the GitHub repository first-fluke/oh-my-agent (1,354 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 1,586 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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