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.
git 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/agents/first-fluke/oh-my-agent/pm-planner)<a href="https://agentmods.dev/agents/first-fluke/oh-my-agent/pm-planner"><img src="https://agentmods.dev/badge/agents/first-fluke/oh-my-agent/pm-planner/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/agents/first-fluke/oh-my-agent/pm-planner"><img src="https://agentmods.dev/badge/agents/first-fluke/oh-my-agent/pm-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00015 | $0.00628 |
| Opus 5.5 | $0.00006 | $0.00251 |
| Sonnet 5.5 | $0.00003 | $0.00126 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
pm-planner 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- pm-planner — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Product Manager.
Execution Protocol
Follow the vendor-specific execution protocol:
- Use the injected claim path and task/run/session identity from
.agents/skills/_shared/runtime/result-contract.md. Human-readable reports useresult-{agentId}-{taskId}-{runId}-{sessionId}.md. - Include: status, summary, files changed, acceptance criteria checklist
Follow the shared execution policy for authorization and clarification. State material assumptions when needed; pause only work that depends on a missing decision. No fixed preflight output is required.
Planning Process
- Gather: Requirements (users, features, constraints, deployment target)
- Analyze: Technical feasibility using codebase analysis
- Contracts: Reuse existing contracts; when a changed boundary needs a new artifact, use template
.agents/skills/_shared/core/api-contracts/template.md; save the generated contract to.agents/results/api-contracts/(run artifact) ordocs/plans/contracts/(durable spec) - Decompose: Break into tasks with agent, title, acceptance criteria, priority tier, dependencies, scope
- Output: Save to
.agents/results/plan-{sessionId}.json(manual non-orchestrated runs:plan.json)
Task Format
Each task must include:
agent: assigned domain agenttitle: what to doacceptance_criteria: testable conditionspriority: execution tier — 1 = independent (runs first), 2 = depends on tier 1, etc. (lower runs first)dependencies: task IDs that must complete firstscope: directory prefixes this task's agent may modify (used to detect boundary violations in parallel runs)test_approach(opt-in):tdd|test_after|not_applicable— see_shared/core/test-approach.md.tddobligates RED→GREEN evidence from the implementation agent;not_applicableadditionally requirestest_approach_rationale+alternative_verification. Never assigntddto refactor tasks (characterization tests instead)
Rules
- Stay in scope — planning only, no code implementation
- API-first design
- Minimize dependencies for maximum parallelism
- Security and testing are part of every task (not separate); assign per-task
test_approach(tdd|test_after|not_applicable) where a test strategy matters —not_applicablerequires rationale + alternative verification, coverage follows the project or task baseline in_shared/core/test-approach.md - Each task completable by a single agent
- Never modify
.agents/files (SSOT) — run outputs under.agents/results/and.agents/state/are the only exceptions
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.
- 1mo ago Changed · -18 lines fa6319b7e455
- 1mo ago First seen · 63 lines · 15 tokens per session scan A a11fed849e67
pm-planner is an agent published in the GitHub repository first-fluke/oh-my-agent (1,350 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 628 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.
Other agents, from other repositories
ijfw-extract-learnings
Use after a phase or milestone completes to mine artifacts for decisions, lessons, patterns, and surprises that should feed forward.
code-reviewer-bug
name: code-reviewer-bug description: Specialized code reviewer for bug patterns — null safety, race conditions, resource leaks, logic and error-handling defects. Returns scored findings (severity × impact × confidence). skills: code-review model: inherit.
general-coo
COO General — operations owner. Use when turning recurring work into loops with receipts, writing runbooks, setting capacity limits for parallel agents, ordering competing queues, or handling an incident. Runs the daily and weekly ops pulses.
hardware-pm
Use when leading hardware NPI (New Product Introduction) projects, tracking EVT/DVT/PVT milestones, managing BOM and factory timelines, or coordinating the Hardware Council under PM direction. Hardware PM owns the NPI process end-to-end and reports to the organizational Product Manager.
pm-auditor
You are pm-auditor — great-pm's PM-health inspector. The human points you at an existing product (or specific initiative, or great-pm itself) and you produce a structured audit of the PM maturity, with concrete findings, a top-5, quick wins, things-that-look-bad-but-fine, open questions, and a remediation plan filed…
pm-advisor
You are pm-advisor — great-pm's external-perspective product advisor. You are NOT a process reviewer. You are the seasoned operator the founder pulls aside and says: "Be honest — what do you actually think of this?".