Borrowing it
Nothing to install: this file belongs to T-rav/hydraflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/T-rav/hydraflow/staging/.codex/skills/hf.test-audit/SKILL.mdgit clone --depth 1 https://github.com/T-rav/hydraflowWrote 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/t-rav/hydraflow/hf.test-audit)<a href="https://agentmods.dev/skills/t-rav/hydraflow/hf.test-audit"><img src="https://agentmods.dev/badge/skills/t-rav/hydraflow/hf.test-audit.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 | $0.00057 | $0.08084 |
| Opus 5 | $0.00028 | $0.04042 |
| Sonnet 5 | $0.00011 | $0.01617 |
| Haiku 4.5 | $0.00006 | $0.00808 |
Grade A, and why
test-audit 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.
How it starts
The opening of the file, as written. The whole thing — 1,155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Audit Agent
Comprehensive test quality auditing agent that analyzes test files for adherence to established patterns, identifies anti-patterns, and suggests improvements based on the codebase's own testing standards.
Agent Purpose
Audit test files across all services (bot, tasks, control_plane, agent-service) to ensure:
- Naming conventions - Files and tests follow established patterns
- 3As structure - Arrange, Act, Assert clarity
- Single responsibility - One logical assertion per test
- Builders & Factories - Proper use and identification of missing opportunities
- Anti-pattern detection - Multiple assertions, over-mocking, incomplete setups
Established Standards (From Codebase Analysis)
1. Factory Pattern
Location: bot/tests/utils/mocks/, bot/tests/utils/settings/, tasks/tests/factories.py
Pattern:
class LLMProviderMockFactory:
"""Factory for creating mock LLM provider objects"""
@staticmethod
def create_mock_provider(response: str = "Test response") -> MagicMock:
"""Create basic mock LLM provider"""
provider = MagicMock()
provider.get_response = AsyncMock(return_value=response)
provider.model = "test-model"
return provider
@staticmethod
def create_provider_with_error(error: Exception) -> MagicMock:
"""Create LLM provider that raises errors"""
provider = LLMProviderMockFactory.create_mock_provider()
provider.get_response = AsyncMock(side_effect=error)
return provider
Standards:
- Static methods only
- Clear method names:
create_<thing>,create_<thing>_with_<condition> - Composition: specialized factories call basic factory
- Return typed objects (MagicMock, AsyncMock, or real objects)
- Docstrings for each method
2. Builder Pattern
Location: bot/tests/utils/builders/
Pattern:
class MessageBuilder:
"""Builder for creating message objects with fluent API"""
def __init__(self):
self._role = "user"
self._content = "Test message"
def as_user(self):
"""Set role as user"""
self._role = "user"
return self
def as_assistant(self):
"""Set role as assistant"""
self._role = "assistant"
return self
def with_content(self, content: str):
"""Set message content"""
self._content = content
return self
def build(self) -> dict[str, Any]:
"""Build the message"""
return {"role": self._role, "content": self._content}
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 · 1,155 lines · 57 tokens per session scan A 1db8d645a5de
test-audit is a skill published in the GitHub repository T-rav/hydraflow (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 57 tokens to every session and 8,084 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
check
Confirm a change before merge. /check verify drives the real app to prove behavior against the spec (every acceptance criterion met, every surface built). /check review runs a senior code review on a fresh model, one that did not write the code. Verify after /develop, review before a PR. Writes to docs/reviews/, never…
holdout-validation
Cross-reference agent self-review claims against actual file state using hidden holdout scenarios, producing mapped P1/P2/P3 findings that reference visible acceptance criteria only. Use when verifying implementation completeness after self-review in start (Phase 4 VERIFY), address (convergence check), or review…
quality-checklist
Validate implementation quality through custom checklists, scoring against constitution standards, specification coverage, and producing remediation recommendations.
orchestrated-execution
Execute work units through the rigorous 4-phase Metaswarm cycle (Implement -> Validate -> Adversarial Review -> Commit) with independent quality gate enforcement.
verification
Verification-before-completion discipline ensuring all success criteria are met, tests pass, and reviews complete before declaring work done.
moai-foundation-quality
TRUST 5 quality principles and how MoAI enforces them through agents, the 3-level harness, /moai gate, and sync-auditor scoring. Use for code review, quality gate checks, coverage targets, or TRUST 5 compliance.