What is eval-reports?

Instructions for running and reporting agent evaluations in a local checkout of camelAI. An agent evaluation is a repeatable test of how an AI agent performs a task.

Use it to run a named evaluation, test a custom prompt, optionally deploy a test app, and report the run’s results.

It explains how to run the tests locally, handle required environment settings, and share results through the project’s read-only dashboard and API. It also supports custom evaluation prompts.

eval-reports — Skill for Claude Code and Codex by qaml-ai: Instructions for running and reporting agent evaluations in a…

Install

Getting it into your agent

There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.

Made for: Claude Code, Codex.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/qaml-ai/camelai/eval-reports.svg)](https://agentmods.dev/skills/qaml-ai/camelai/eval-reports)

Your own site

<a href="https://agentmods.dev/skills/qaml-ai/camelai/eval-reports"><img src="https://agentmods.dev/badge/skills/qaml-ai/camelai/eval-reports.svg" alt="Measured on agentmods" height="20"></a>

Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,616 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. 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.

Model	Per session	Once invoked
Fable 5.1	$0.00000	$0.01616
Opus 5	$0.00000	$0.00808
Sonnet 5	$0.00000	$0.00323
Haiku 4.5	$0.00000	$0.00162

Measured 6d ago against content hash 48bcce7852c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

eval-reports scanned grade A with 1 finding 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.

The scan reads SKILL.md. This mod also ships 11 executable files (app/lib/api.ts, app/lib/batches.ts, app/lib/format.ts, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

`curl -H "CF-Access-Client-Id: ..." -H "CF-Access-Client-Secret: ..."` both use these.

workers/eval-reports/SKILL.md · 114 lines

How it starts

The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Running agent evals

camelAI agent evals run locally in a qaml-ai/camelAI checkout — there is no remote runner. This service (https://evals.camelai.dev, workers/eval-reports) is only the shared results history: a read-only dashboard + JSON API plus an upload endpoint the local reporter uses. Everything is behind Cloudflare Access.

This document is served by the service itself at GET /skill, so it always matches the running API. (You're reading it because you fetched that endpoint.)

Run an eval (locally, in chiridion-app)

Requirements: Docker, Bun, and a .dev.vars in the repo root (the eval secret bundle).

bun run test:eval <eval-id>          # ids from workers/main/tests/evals/manifest.json
bun run test:eval:dashboard          # or :deploy / :sandbox shortcuts

# Custom prompt (not committed to the tree) via the generic harness:
CUSTOM_EVAL_PROMPT="Build a dashboard from fake data." \
  bun scripts/run-agent-eval.mjs custom-prompt-live

Common knobs: --model <id>, --timeout-ms <ms>, EVAL_REAL_DEPLOY=0/1 (publish apps to the testing-grounds namespace for real), CUSTOM_EVAL_PROJECT, CUSTOM_EVAL_REQUIRED_TRANSCRIPT_SUBSTRINGS. See bun scripts/run-agent-eval.mjs --help.

Suite and matrix invocations automatically mint a shared EVAL_BATCH_ID plus a default EVAL_BATCH_LABEL, so their member runs group together on the dashboard. Pre-set either env var when an orchestrator needs to join runs into an existing batch. Solo bun run test:eval <id> runs stay batchless and render as singleton batches.

Report a run here

Set EVAL_REPORT=1 on the run and the reporter (scripts/report-eval-run.mjs) uploads the output log and run metadata when the eval finishes — pass or fail. It also uploads the transcript artifact, including its scorecard, when the eval emitted it:

EVAL_REPORT=1 bun run test:eval dashboard-fake-data-live

Reporting is best-effort and never fails the eval. Artifactless harness failures are still reported; ingest synthesizes an evaluation_contract failure so they remain visible. Finalization is retried up to three times with the same run id before the reporter gives up. Re-report an artifact by hand:

Read the full file on GitHub · 114 lines

Files

What ships with it

42 files 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.

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.

6d ago First seen · 114 lines · 0 tokens per session scan A 48bcce7852c4

Subscribe to this mod's changes

eval-reports is a skill published in the GitHub repository qaml-ai/camelAI (365 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,616 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Testing

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eval-reports