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 athola/claude-night-market --skill quality-gategit clone --depth 1 https://github.com/athola/claude-night-marketWrote 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/athola/claude-night-market/quality-gate)<a href="https://agentmods.dev/skills/athola/claude-night-market/quality-gate"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/quality-gate/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/athola/claude-night-market/quality-gate"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/quality-gate.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 | $0.00039 | $0.01577 |
| Opus 5 | $0.00019 | $0.00788 |
| Sonnet 5 | $0.00008 | $0.00315 |
| Haiku 4.5 | $0.00004 | $0.00158 |
Grade A, and why
quality-gate 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 9d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quality Gate
When To Use
- Running quality checks on egregore work items
- Self-review before creating a PR
- Reviewing another agent's PR in PR-review mode
When NOT To Use
- Manual code reviews outside egregore pipeline
- One-off lint or format checks (use
make lintdirectly)
Orchestrate the QUALITY stage of egregore's pipeline. Each quality step runs convention checks from the codex and invokes mapped skills.
Routing Table
| Step | Conventions | Skills | Modes |
|---|---|---|---|
| code-review | C1,C2,C3,C4,C5 | pensive:unified-review | self, pr |
| unbloat | - | conserve:unbloat | self |
| code-refinement | - | pensive:code-refinement | self |
| update-tests | - | sanctum:update-tests | self |
| update-docs | C5 | sanctum:update-docs, scribe:slop-detector | self |
Inputs
The orchestrator invokes this skill with:
- step: which quality step to run (e.g. "code-review")
- mode: "self-review" or "pr-review"
- work_item_id: the manifest work item ID
- branch: the git branch with changes
- pr_number: (PR-review mode only) the PR number
Self-Review Workflow
When mode is "self-review":
- Get changed files:
git diff --name-only main...HEAD - Load conventions from
conventions/codex.yml - Filter conventions to those mapped to the current step
- Run convention checks via
conventions.py - Invoke mapped skills on the changed files
- Collect all findings
- Calculate verdict
Auto-Fix Loop
If blocking findings exist:
- Attempt to fix each finding (skill-dependent)
- Commit fixes to the work item branch
- Re-run convention checks
- If still blocking after 3 attempts, verdict is "fix-required"
Verdict Calculation
if no findings:
verdict = "pass"
elif all findings are severity "warning":
verdict = "pass-with-warnings"
elif blocking findings remain after auto-fix:
verdict = "fix-required"
Record verdict in manifest decisions:
{
"step": "code-review",
"chose": "pass-with-warnings",
"why": "2 warnings (C4: noqa in hooks), 0 blocking"
}
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.
- 9d ago First seen · 198 lines · 39 tokens per session scan A 8b21ced55bb6
quality-gate is a skill published in the GitHub repository athola/claude-night-market (337 stars, last pushed 2d ago), licensed MIT. It adds 39 tokens to every session and 1,577 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
code-review
Orchestrates code review by detecting the project language and architecture, then routing to the appropriate specialized review skill (code-review-go, code-review-typescript, code-review-python, code-review-php, code-review-architecture). Falls back to the generic checklist when no specific skill applies. Invoked when…
review-orchestrator
Get multiple perspectives on your work — coordinate reviews across cognitive modes.
plugin-quality
A review guide for checking Zhin.js plugins before release. It covers plugin structure, feature declarations, resource cleanup, message sending, and security.
testing-anti-patterns
Reviews test code to identify and fix common testing anti-patterns including flaky tests, over-mocking, brittle assertions, test interdependency, and hidden test logic. Flags bad patterns, explains the specific defect, and provides corrected implementations. Use when reviewing test code, debugging intermittent or…
code-review
Reviews diffs by severity to produce actionable feedback.
qa-expert-review
A final expert check of AI-generated test cases for business correctness, completeness, clarity, and ease of execution.