Borrowing it
Nothing to install: this file belongs to silver-rain-dev/sk-wwise-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/silver-rain-dev/sk-wwise-mcp/main/.claude/skills/eval-batch/SKILL.mdgit clone --depth 1 https://github.com/silver-rain-dev/sk-wwise-mcpWrote 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/silver-rain-dev/sk-wwise-mcp/eval-batch)<a href="https://agentmods.dev/skills/silver-rain-dev/sk-wwise-mcp/eval-batch"><img src="https://agentmods.dev/badge/skills/silver-rain-dev/sk-wwise-mcp/eval-batch.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 5.1 | $0.00032 | $0.00891 |
| Opus 5 | $0.00016 | $0.00445 |
| Sonnet 5 | $0.00006 | $0.00178 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
eval-batch 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 6d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This is a TEST/EVAL SKILL ONLY. Do NOT use for production MCP usage. Runs up to 5 test cases per invocation, picking up where the last batch left off.
Running all cases automatically
Use /loop to iterate through every test case unattended:
/loop 60s /eval-batch
Each iteration runs up to 5 cases. With 39 total cases, this finishes in ~8 iterations. When all cases are tested the skill prints "All cases evaluated", auto-generates the report, and tells you to stop the loop.
How it differs from /eval-routing
/eval-routing— 1 case per invocation (fine-grained,/loop 30s)/eval-batch— up to 5 cases per invocation (faster,/loop 60s)
Both pick up where they left off using test_results.json.
Instructions
-
Read
tests/eval/test_cases.jsonto get all test cases -
Read
tests/eval/test_results.jsonto find which cases have already been tested- If the file doesn't exist, no cases have been tested yet
-
Stale-result guard: If ALL cases already have results, auto-reset by running:
python tests/eval/verify.py --check-staleThis will detect stale results (from a prior session) and reset automatically. Then re-readtest_results.json(it will now be empty). -
Find the next case (by
id) that has no entry in test_results -
If all cases are tested:
- Run
python tests/eval/report.pyto generate the report inline - Print "All cases evaluated" and tell the user to stop the loop (
CronDelete <id>) - Stop — do not run any more cases
- Run
-
Determine the batch: take the next 5 untested cases (or fewer if less than 5 remain).
-
For each case in the batch (in order by id):
a. Clear
tests/eval/tool_log.jsonl(write empty string to it)b. Handle the test case prompt as if a real user asked it:
- The prompt is in the
"prompt"field of the test case - Use MCP tools normally to fulfill the request
- For write operations (create, delete, move), always clean up after:
- Check if the case has a
"cleanup"field in test_cases.json - If
cleanup.toolis"set_wwise_object_properties": restore the original value usingset_wwise_object_properties(operations=[{"object": cleanup.object, "properties": {cleanup.property: cleanup.restore_value}}]) - If
cleanup.toolis"set_wwise_object_name": rename back usingset_wwise_object_name(object=cleanup.object_after, value=cleanup.restore_name) - If
cleanup.bulk_restoreexists: iterate and restore each entry - If no cleanup field: use your judgment (e.g., delete created objects)
- Check if the case has a
- Cases with
"expected_error": truewill have the tool fail at runtime — that's OK. The eval only checks routing (which tools were called), not whether they succeeded.
- The prompt is in the
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 · 75 lines · 32 tokens per session scan A 9868fa68ae8c
eval-batch is a skill published in the GitHub repository silver-rain-dev/sk-wwise-mcp (5 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 891 once invoked, about $0.0002 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.
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