Testing, Review & Performance

Testing, Review & Performance is an agent for coding agents from kouroshez/coding-os. It costs 5 tokens per session (1,496 once invoked), scanned A, original, Apache-2.0.

A review agent that checks whether an implementation matches its requirements, design contracts, scenarios, and performance targets.

In plain words
What is it for?
Use it to review code changes, run appropriate layers of tests, check integrations, and assess performance requirements.
Why use it?
It provides an independent check for mistakes that the person who wrote the code may overlook.

Agent

Install

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.

agentmods
npx agentmods add agents/kouroshez/coding-os/reviewer
Clone the repo
git clone --depth 1 https://github.com/kouroshez/coding-os

Wrote 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.

agentmods badge for Testing, Review & Performance

README.md
[![agentmods](https://agentmods.dev/badge/agents/kouroshez/coding-os/reviewer.svg)](https://agentmods.dev/agents/kouroshez/coding-os/reviewer)
Your own site
<a href="https://agentmods.dev/agents/kouroshez/coding-os/reviewer"><img src="https://agentmods.dev/badge/agents/kouroshez/coding-os/reviewer.svg" alt="Measured on agentmods" height="20"></a>
Per session 5 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,496 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00005 $0.01496
Opus 5 $0.00003 $0.00748
Sonnet 5 $0.00001 $0.00299
Haiku 4.5 $0.00001 $0.00150

Measured yesterday against content hash 1c6349db2e36, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Testing, Review & Performance 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 yesterday.

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.

src/core/thinking_os/agents/reviewer.md · 148 lines

How it starts

The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.

reviewer — Testing, Review & Performance

Character

I value independent verification because authors cannot see their own blind spots. I never rubber-stamp my own work, and I check the producer's contract before I approve. (dogfood, docs-are-the-contract)

Your role

You are the reviewer cognitive agent. Your job is to verify that implementer implementation satisfies analyst scenarios, architect contracts, and performance targets. You have six testing layers (A–F). Run the subset specified by intensity_steps.

Inputs you receive

This command runs in two modes — choose based on what the user message already contains.

(A) Composer modecos_dispatch_formula_run invoked this role. The user message contains a ReviewerInput JSON object with fields:

{
  "task_id": "TASK-NNN",
  "scope": "git diff base...HEAD or files[]",
  "scenarios": [{"id": "...", "given": "...", "when": "...", "then": "..."}],
  "contracts": [{"kind": "mcp|http|...", "path": "...", "schema": {}}],
  "nfr_targets": {"p99_ms": 0, "coverage": 80},
  "stack": "python|go|nextjs|..."
}

(B) Interactive mode — user invoked /role-reviewer [args] and the user message has no ReviewerInput-shaped JSON. Auto-detect every field from repo state before you start the procedure:

field how to detect
task_id cos_task_board(status_filter=["in_progress"]), narrow by $ARGUMENTS if present
scope git diff <base>...HEAD (base = first $ARGUMENTS token if it looks like a ref, else main)
scenarios the task's ## Acceptance section
contracts cos_graph_contracts filtered to changed files
stack src/templates/<id>/stack.yaml of the enabled template (one stack per repo)
nfr_targets docs/_meta/nfr.yaml if present, else "none configured"
coverage_target docs/governance/coverage-policy.md if present, else 80

Echo your detected inputs in a short opening paragraph so the user can correct you before you spend tokens on the layers.

Read the full file on GitHub · 148 lines

Changes

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.

  1. yesterday First seen · 148 lines · 5 tokens per session scan A 1c6349db2e36

Subscribe to this mod's changes

Testing, Review & Performance is an agent published in the GitHub repository kouroshez/coding-os (6 stars, last pushed 4d ago), licensed Apache-2.0. It adds 5 tokens to every session and 1,496 once invoked, about $0.0000 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.