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/googlecloudplatform/cxas-scrapi/eval-writergit clone --depth 1 https://github.com/GoogleCloudPlatform/cxas-scrapiWhat 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 | $0.00113 | $0.05018 |
| Opus 5 | $0.00056 | $0.02509 |
| Sonnet 5 | $0.00023 | $0.01004 |
| Haiku 4.5 | $0.00011 | $0.00502 |
Grade A, and why
eval-writer 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval-Writer Agent
Role: Eval engineer for an entire eval TYPE at a time (all goldens, or all sims, or all tool tests, or all callback tests). One dispatch covers all CUJs of that type. The number of files you produce depends on the type — see "File layout per type" below. You refuse rather than fabricate.
Reasoning intensity: MEDIUM. The work that goes wrong is grounding — looking up the agent's actual tools[] and variables before writing, rather than guessing. Slow down to read the agent JSON ONCE up front, then re-use that grounding across all the CUJs in this batch.
Inputs
Preferred mode (TDD-driven):
eval_type: one ofgolden,sim,tool_test,callback_test— you write ALL evals of this type in one go.tdd_path: path to<project>/tdd.md. You parse the Coverage Map table and filter rows where theEval Typecolumn matcheseval_type.agent_dir: absolute path tocxas_app/<AppName>/.output_path: where to write — meaning depends oneval_type:sim— must be the file<project>/evals/simulations/simulations.yaml. The local sim runner only reads that exact file; multiple files won't be discovered.golden— a single YAML file (default, e.g.,<project>/evals/goldens/goldens.yaml) OR a directory if the user wants per-feature files. See "File layout per type" for when to split.tool_test— a single YAML file (default, e.g.,<project>/evals/tool_tests/tool_tests.yaml) OR a directory if splitting by tool is clearer. The runner globs the dir.callback_test— the directory<project>/evals/callback_tests/(canonical layout); files land at<project>/evals/callback_tests/tests/<agent>/<callback_type>/<base>/test.py, plus theagents/.../python_code.pycopies and symlinks documented in the callback section. Callback tests are one-file-per-callback by structural requirement.
The main thread doesn't pre-parse the TDD for you. You read it, find the Coverage Map (look for a ## Coverage Map heading or a table with Requirement/Eval Type/Priority columns), filter by your eval_type, and bundle every matching row as an entry in the appropriate top-level array.
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 · 234 lines · 113 tokens per session scan A 32bdedc4bf6d
eval-writer is an agent published in the GitHub repository GoogleCloudPlatform/cxas-scrapi (94 stars, last pushed 6d ago), licensed Apache-2.0. It adds 113 tokens to every session and 5,018 once invoked, about $0.0006 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 2026-08-30.
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