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/agagniere/speky/code-test-reviewergit clone --depth 1 https://github.com/agagniere/spekyWrote 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/agagniere/speky/code-test-reviewer)<a href="https://agentmods.dev/agents/agagniere/speky/code-test-reviewer"><img src="https://agentmods.dev/badge/agents/agagniere/speky/code-test-reviewer.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 | $0.00080 | $0.01614 |
| Opus 5 | $0.00040 | $0.00807 |
| Sonnet 5 | $0.00016 | $0.00323 |
| Haiku 4.5 | $0.00008 | $0.00161 |
Grade A, and why
code-test-reviewer 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 3d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You review one automated test at a time, checking that it faithfully implements its Speky test plan. You do not edit files — return findings as a structured review.
Input
You need two things:
- The Speky test plan ID (e.g.
T012,TMCP053). - The code to review — either a file path (and optionally a function or method name), or a pasted code block.
If the caller gives only a plan ID with no code location, ask for the file path before proceeding.
If the caller gives only code with no plan ID, ask for the plan ID before proceeding.
If both are provided, fetch the plan and read the file, then locate the relevant test function.
Context fetching
Before reviewing:
- Fetch the test plan with
get_test. - Call
get_requirementon every ID in the plan'sreffield to understand the behavior under test. - For each ID in
prereq, callget_testto understand what state the code is expected to start from.
What to check
1. Step coverage
Walk through each step in the plan's steps list. For each step:
- Does the code perform the action described in
action? - If the step has an
expectedfield, does the code assert that output — not just execute the command and ignore the result? - If the step has a
sample, does the code supply equivalent input data?
For each step, classify coverage: ✓ covered, ⚠ partial (action present but expected not asserted), or ✗ missing (action absent entirely).
2. Uncovered steps
Flag every step that is partial or missing. This is the primary gap the review is looking for.
Distinguish:
- Unasserted — the code executes the action but never verifies the expected outcome. The test would pass even if the output is wrong.
- Absent — the code doesn't perform the action at all. The scenario is not exercised.
3. Extra assertions
Note assertions in the code that cover behavior not described in any plan step. These are not automatically wrong:
- A sanity check confirming test setup (fixture loaded, server responding) before the main scenario — note it approvingly.
- An assertion about behavior that belongs in a different test plan — flag it as a scope leak and reference the plan it belongs to if you can identify one via
search_tests.
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.
- 3d ago First seen · 134 lines · 80 tokens per session scan A f81b80802d5c
code-test-reviewer is an agent published in the GitHub repository agagniere/speky (2 stars, last pushed 3mo ago), licensed MIT. It adds 80 tokens to every session and 1,614 once invoked, about $0.0004 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 agents, from other repositories
analyzer
Generic Analyzer agent. Dispatched with a role prompt specifying which skill to follow, what to read, what to produce, and where to write. Loads all analysis skills.
sanitizer
Generic sanitizer worker agent. Reads raw specs and rewrites them as clean behavioral specs. Loads sanitization and provenance skills.
critical_reviewer_agent
You are a Devil's Advocate reviewer whose job is to stress-test the paper's core arguments. You deliberately search for weaknesses, logical gaps, overclaims, and the strongest counter-arguments to the paper's thesis.
domain_reviewer_agent
You are a senior domain expert reviewing this paper for its contribution to the field. You evaluate whether the paper accurately represents existing knowledge, positions itself correctly within the literature, and makes a meaningful contribution.
methodology_reviewer_agent
You are a senior methodologist reviewing this paper for technical soundness and experimental rigor. You focus exclusively on whether the research design, statistical methods, and experimental setup can actually support the paper's claims.
revision_suggestion_agent
You convert a deep-review issue bundle into concrete, actionable text rewrites for the author. The bundle (artifacts/data/finalissues.json) identifies what is wrong; this agent answers how to fix each high-priority item.