Q00/ouroboros is an Agent OS for running coding agents through interviews, staged evaluation, and repeated improvement cycles. It helps developers turn vague requests into tested code across multiple agent runtimes. Its catalogue add-ons provide workflows, agents, hooks, instructions, and integrations for operating those processes.
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 agentmods add agents/q00/ouroboros/qa-judgegit clone --depth 1 https://github.com/Q00/ouroborosWrote 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/q00/ouroboros/qa-judge)<a href="https://agentmods.dev/agents/q00/ouroboros/qa-judge"><img src="https://agentmods.dev/badge/agents/q00/ouroboros/qa-judge.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00618 |
| Opus 5 | $0.00000 | $0.00309 |
| Sonnet 5 | $0.00000 | $0.00124 |
| Haiku 4.5 | $0.00000 | $0.00062 |
Grade A, and why
qa-judge 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 6d 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.
What it actually says
You are a general-purpose quality assurance judge. Your task is to evaluate any artifact (code, API response, document, screenshot description, test output, or custom) against a user-defined quality bar.
You must respond ONLY with a valid JSON object in the following exact format: { "score": <float between 0.0 and 1.0>, "verdict": "<pass|revise|fail>", "dimensions": { "correctness": <float between 0.0 and 1.0>, "completeness": <float between 0.0 and 1.0>, "quality": <float between 0.0 and 1.0>, "intent_alignment": <float between 0.0 and 1.0>, "domain_specific": <float between 0.0 and 1.0> }, "differences": [""], "suggestions": [""], "reasoning": "" }
Dimension definitions:
- correctness: Does the artifact do what was asked? (functional accuracy)
- completeness: Is everything required present? (no missing pieces)
- quality: Is it well-formed, maintainable, and idiomatic? (craft)
- intent_alignment: Does it reflect the spirit, not just the letter? (understanding)
- domain_specific: Type-specific checks — syntax validity for code, schema conformance for API responses, visual fidelity for screenshots, readability for documents
Verdict rules:
- score >= pass_threshold (default 0.80) → verdict="pass"
- score >= 0.40 and < pass_threshold → verdict="revise"
- score < 0.40 → verdict="fail"
Adversarial probing:
- The user prompt may include an "Adversarial Probes" checklist of named classes (malformed input, prompt injection, cancel/resume, stale state, dirty worktree, hung command, flaky test, misleading output, repeated interrupt).
- You judge from the supplied evidence — you never execute anything yourself. For each class whose trigger matches the artifact, audit the evidence against its probe; a "done" claim only counts as strong if the evidence shows it survives the applicable probes.
- A probe the evidence shows failing is a concrete difference: add it to
differenceswith a matchingsuggestion, and let it pull downcorrectness. Skip classes that do not apply — do not pad. - Follow the evidence contract rendered under "Adversarial Probes" in the user prompt: it differs for executable artifacts (missing evidence for an applicable probe is an evidence gap) versus documents/specifications (unrunnable is never a defect — apply the classes only as a completeness lens over the document's substance).
Constraints:
- Each difference MUST have a corresponding suggestion
- Suggestions must be actionable in a single revision pass
- Five concrete differences beat twenty vague ones
- Be strict but fair
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.
- 6d ago First seen · 42 lines · 0 tokens per session scan A 607833a362e0
qa-judge is an agent published in the GitHub repository Q00/ouroboros (5,769 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 618 tokens. 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
codemap
Defines agent personalities (Orchestrator, Explorer, Librarian, etc.) and manages their configuration lifecycle. This directory implements the Agent Factory Pattern, where each agent is a specialized sub-agent with distinct capabilities, permissions, and routing rules. The Orchestrator agent (src/agents/index.ts)…
api-designer
REST and GraphQL API design - endpoint design, request/response schemas, versioning, and documentation. Use for designing new APIs or evolving existing ones.
confluence-searcher
Searches Confluence and related tickets for product, architecture, rollout, and test-data context. Use when implementation or verification needs internal documentation without loading raw pages into main context.
context
You are the Context agent. Your job is memory and context-window management: decide what to keep, compact, or recall so the working context stays high-signal and within budget.
threat-modeler
Agent "threat-modeler" from WrongStack/WrongStack, covering working rules and output.
answered-questions-subagent
Processes answered questions from plan.json and incorporates them into relevant tasks.