sk-wwise-mcp: Skill for Claude Code

.claude/skills/eval-batch/SKILL.md

eval-batch is a skill for Claude Code from silver-rain-dev/sk-wwise-mcp. It costs 32 tokens per session (891 once invoked), scanned A, original, Apache-2.0.

A test-only procedure that runs up to five evaluation cases at a time and remembers which cases are finished.

In plain words
What is it for?
Use it to evaluate routing or other test cases in groups, continue from saved results, and generate a report when all cases are complete.
Why use it?
It avoids repeating completed cases and provides a faster way to run a full test set in batches.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

This is silver-rain-dev/sk-wwise-mcp's own configuration. It tells Claude Code how to work on sk-wwise-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything sk-wwise-mcp configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/silver-rain-dev/sk-wwise-mcp/main/.claude/skills/eval-batch/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/silver-rain-dev/sk-wwise-mcp

Made for: Claude Code.

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 eval-batch

README.md
[![agentmods](https://agentmods.dev/badge/skills/silver-rain-dev/sk-wwise-mcp/eval-batch.svg)](https://agentmods.dev/skills/silver-rain-dev/sk-wwise-mcp/eval-batch)
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<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>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 891 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 5.1 $0.00032 $0.00891
Opus 5 $0.00016 $0.00445
Sonnet 5 $0.00006 $0.00178
Haiku 4.5 $0.00003 $0.00089

Measured 6d ago against content hash 9868fa68ae8c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

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.

.claude/skills/eval-batch/SKILL.md · 75 lines

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

  1. Read tests/eval/test_cases.json to get all test cases

  2. Read tests/eval/test_results.json to find which cases have already been tested

    • If the file doesn't exist, no cases have been tested yet
  3. Stale-result guard: If ALL cases already have results, auto-reset by running: python tests/eval/verify.py --check-stale This will detect stale results (from a prior session) and reset automatically. Then re-read test_results.json (it will now be empty).

  4. Find the next case (by id) that has no entry in test_results

  5. If all cases are tested:

    • Run python tests/eval/report.py to 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
  6. Determine the batch: take the next 5 untested cases (or fewer if less than 5 remain).

  7. 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.tool is "set_wwise_object_properties": restore the original value using set_wwise_object_properties(operations=[{"object": cleanup.object, "properties": {cleanup.property: cleanup.restore_value}}])
      • If cleanup.tool is "set_wwise_object_name": rename back using set_wwise_object_name(object=cleanup.object_after, value=cleanup.restore_name)
      • If cleanup.bulk_restore exists: iterate and restore each entry
      • If no cleanup field: use your judgment (e.g., delete created objects)
    • Cases with "expected_error": true will have the tool fail at runtime — that's OK. The eval only checks routing (which tools were called), not whether they succeeded.

Read the full file on GitHub · 75 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. 6d ago First seen · 75 lines · 32 tokens per session scan A 9868fa68ae8c

Subscribe to this mod's changes

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.