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-marketgit 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-market)<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-market"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-market/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-market"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-market.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 131 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00152 | $0.02426 |
| Opus 5 | $0.00076 | $0.01213 |
| Sonnet 5 | $0.00030 | $0.00485 |
| Haiku 4.5 | $0.00015 | $0.00243 |
Grade A, and why
oma-market 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 2d 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:
- oma-market — 100% identical, 23 lines differ
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Research Agent - Community Signal Intelligence
Scheduling
Goal
Run the upstream last30days research engine (always the latest release, managed by oma) for community-signal research, then frame the result for the user's intent (pain / trend / competitor / discovery) with strategic frameworks and save one brief under .agents/results/market/.
Intent signature
- User asks about pain points, user complaints, or voice-of-customer signals for a product or category.
- User asks what is trending, growing, or declining in a space this week or month.
- User asks how one product compares to another in community sentiment or positioning.
- User asks for discovery or exploratory market research on a topic, a person, a company, or a ticker.
When to use
- Extracting real user pain points from community posts (Reddit with real upvotes and top comments, HN, X, Bluesky, GitHub Issues)
- Detecting trends in a category over a window (
--days 7|30|90|180) - Competitor sentiment analysis and SWOT / Porter's 5F positioning
- Open-ended discovery research (
--discover), person mode, hiring signals (--hiring-signals), follow-up drills (--drill)
When NOT to use
- General web research without market framing -> use oma-search directly
- Academic literature -> use oma-scholar
- Live dashboards or scheduled monitoring ->
oma schedule <action>wrapping this skill
Expected inputs
- Topic string; optional
--intent pain|trend|competitor|discovery(else classified perresources/intent-rules.md) - Optional window (
--days),--vs <entity>(competitor),--frameworks auto|none|swot,5f,pestel - Any native last30days flag (see
oma market run --help) — passed through verbatim
Expected outputs
- Single markdown brief at
.agents/results/market/{topic-slug}-{YYYYMMDD}.md - First line: the engine's badge (
🌐 last30days v{VERSION} · synced {date}); body per the upstream OUTPUT CONTRACT; framework sections appended per intent; engine footer preserved - Raw engine artifacts under
market.save_dir(default.agents/results/market/raw/)
What ships with it
8 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.
- 2d ago Changed · +15 lines d2636405df16
- 5d ago Changed fbac88f70123
- 12d ago First seen · 132 lines · 152 tokens per session scan A 195597515e00
oma-market is a skill published in the GitHub repository first-fluke/oh-my-agent (1,283 stars, last pushed yesterday), licensed MIT. It adds 152 tokens to every session and 2,426 once invoked, about $0.0008 per session on Opus 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 skills, from other repositories
agent-harness-fault-injection
Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
ai-fleet-project-execution
AI fleet project execution (orchestrator=PM, marketing agent, backend dev agent, video agent). Fast-iteration architecture pivots and inter-agent task delegation across multi-hour sessions. Use when a user assigns a multi-agent project with "take it as a team" instruction.
memoria-heartbeat
Minden körben átnézi az ELŐZŐ KÖR ÓTA történteket, menti a fontosat, és skill-eket generál ha volt komplex munka.
github-pr-rebase-merge
Merge a stack of GitHub PRs sequentially when they share files and will cause cascading conflicts. Triggers when user says "merge the PRs sorban" or similar, and the PRs come from external forks (cannot push back to PR branch).
handoff
Generate a HANDOFF.md context transfer document for session continuity. Use when switching sessions, handing off to another agent, or preserving complex task context before a context window reset. Trigger on "/handoff" command or "handoff:" prefix in inter-agent messages.
intel-collector
Proaktív hírszerző -- óránkénti gyűjtés a saját figyelt témáidban, eredmény az intel registry-be (store/intel.db). A napi brief (intel-napi-brief) ebből épül.