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 frabcd/codex-ai-game-studio --skill test-evidence-reviewgit clone --depth 1 https://github.com/frabcd/codex-ai-game-studioWrote 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/frabcd/codex-ai-game-studio/test-evidence-review)<a href="https://agentmods.dev/skills/frabcd/codex-ai-game-studio/test-evidence-review"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/test-evidence-review/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/frabcd/codex-ai-game-studio/test-evidence-review"><img src="https://agentmods.dev/badge/skills/frabcd/codex-ai-game-studio/test-evidence-review.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 | $0.00059 | $0.02240 |
| Opus 5.5 | $0.00024 | $0.00896 |
| Sonnet 5.5 | $0.00012 | $0.00448 |
| Haiku 4.5 | $0.00006 | $0.00224 |
Grade A, and why
test-evidence-review 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.
This is a copy
94% identical to test-evidence-review — 211 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Port provenance: adapted from the pinned upstream source at
984023ddac0d5e27624f2baacde6105e45de375funder MIT; see the repository parity ledger for the exact path and blob.
Test Evidence Review
$ai-game-studio:smoke-check verifies that test files exist and pass. This skill
goes further — it reviews the quality of those tests and evidence documents.
A test file that exists and passes may still leave critical behaviour uncovered.
A manual evidence doc that exists may lack the sign-offs required for closure.
Output: Summary report (in conversation) + optional production/qa/evidence-review-[date].md
When to run:
- Before QA hand-off sign-off (
$ai-game-studio:team-qaPhase 5) - On any story where test quality is in question
- As part of milestone review for Logic and Integration story quality audit
1. Parse Arguments
Modes:
$ai-game-studio:test-evidence-review [story-path]— review a single story's evidence$ai-game-studio:test-evidence-review sprint— review all stories in the current sprint$ai-game-studio:test-evidence-review [system-name]— review all stories in an epic/system- No argument — ask which scope: "Single story", "Current sprint", "A system"
2. Load Stories in Scope
Based on the argument:
Single story: Read the story file directly. Extract: Story Type, Test Evidence section, story slug, system name.
Sprint: Read the most recently modified file in production/sprints/.
Extract the list of story file paths from the sprint plan. Read each story file.
System: file discovery production/epics/[system-name]/story-*.md. Read each.
For each story, collect:
Type:field (Logic / Integration / Visual/Feel / UI / Config/Data)## Test Evidencesection — the stated expected test file path or evidence doc- Story slug (from file name)
- System name (from directory path)
- Acceptance Criteria list (all checkbox items)
3. Locate Evidence Files
For each story, find the evidence:
What ships with it
1 file 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.
- 1mo ago First seen · 254 lines · 59 tokens per session scan A 164fb8adc17f
test-evidence-review is a skill published in the GitHub repository frabcd/codex-ai-game-studio (18 stars, last pushed yesterday), licensed MIT. It adds 59 tokens to every session and 2,240 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to test-evidence-review, differing in 211 lines, and is treated as a copy.
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