CWC Workshops is a collection of materials from Anthropic-run workshops on building and evaluating AI-assisted coding workflows. The workshops cover model selection, multi-agent systems, managed agents, and product development with coding agents. The catalogue entries are examples and teaching materials from those workflows.
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 skills add anthropics/cwc-workshops --skill eval-audit-and-sweepgit clone --depth 1 https://github.com/anthropics/cwc-workshopsWrote 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/anthropics/cwc-workshops/eval-audit-and-sweep)<a href="https://agentmods.dev/skills/anthropics/cwc-workshops/eval-audit-and-sweep"><img src="https://agentmods.dev/badge/skills/anthropics/cwc-workshops/eval-audit-and-sweep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anthropics/cwc-workshops/eval-audit-and-sweep"><img src="https://agentmods.dev/badge/skills/anthropics/cwc-workshops/eval-audit-and-sweep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00692 |
| Opus 5 | $0.00000 | $0.00346 |
| Sonnet 5 | $0.00000 | $0.00138 |
| Haiku 4.5 | $0.00000 | $0.00069 |
Grade A, and why
eval-audit-and-sweep 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 11d 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 — 28 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: eval-audit-and-sweep description: This skill should be used when a user wants to (a) audit the quality and reliability of an existing LLM evaluation suite, or (b) determine which Claude model and inference parameters give the best quality-per-dollar and quality-per-second for their specific task by running a parameter sweep over that eval. Applicable to any eval framework (custom harnesses, tau-bench, inspect-ai, pytest-based, etc.) since the guidance is framework-agnostic.
Eval Audit and Sweep
This skill is an example exercise for the "Picking the Right Model" workshop during Code with Claude. It is a two-phase playbook for getting trustworthy cost-quality numbers out of an existing LLM eval. Phase 1 audits the eval for common reliability issues. Phase 2 wraps it in a model/parameter sweep and produces a recommendation. The phases are independent: a user may ask for only the audit, only the sweep, or both.
The skill contains guidance and example snippets only; it ships no runnable scripts, because every eval framework is structured differently. Claude is expected to read the user's eval code, apply the principles in the reference files, and write whatever glue code that specific codebase needs.
How to use this skill
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Locate the eval. Find the golden set, the judge/scoring function, and the entrypoint that runs one full pass. If the user has not pointed at a specific directory, ask.
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Decide which phase(s) apply. If the user says "is my eval any good" or "review my eval," run Phase 1. If they say "which model should I use" or "what's the cheapest config that still passes," run Phase 2. If they say both or it is ambiguous, run Phase 1 first (a sweep over a broken eval produces misleading numbers).
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Read the relevant reference file before acting:
references/audit.mdfor Phase 1: the health-check checklist (task design, harness design, metrics hygiene, and grader design including LLM-judge biases) and how to report findings in measured, non-dogmatic language.references/sweep.mdfor Phase 2: choosing the grid, instrumenting per-cell metrics, plotting, and stating a one-sentence recommendation. That file links out to harness-specific companions where they exist.
What ships with it
3 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.
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.
- 11d ago First seen · 28 lines · 0 tokens per session scan A e8f8a7e3ae16
eval-audit-and-sweep is a skill published in the GitHub repository anthropics/cwc-workshops (2,051 stars, last pushed 14d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 692 tokens. 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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