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 commands/timothywarner-org/claude-code/eval-promptsgit clone --depth 1 https://github.com/timothywarner-org/claude-codeWhat 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 | $0.00014 | $0.00235 |
| Opus 5 | $0.00007 | $0.00118 |
| Sonnet 5 | $0.00003 | $0.00047 |
| Haiku 4.5 | $0.00001 | $0.00023 |
Grade A, and why
eval-prompts 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.
What it actually says
/eval-prompts
Score the current GenAI app's outputs before it ships. Eval cases file: $1 (default to the genai-prompt-eval skill's resources/templates/eval_cases.jsonl if empty).
- Invoke the
genai-prompt-evalskill. - Load the eval cases from $1, run each through the model, and score the four dimensions: groundedness, relevance, coherence, safety.
- Report a per-dimension score, the pass threshold, and a clear PASS or FAIL per dimension. Do not signal pass/fail by color alone; use the words.
- On any FAIL, name the specific cases that dragged the score down so the prompt or grounding can be fixed. This is the same gate
/deploy-genairuns before it ships. See [[testing]] and [[genai-prompt-eval]].
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 · 14 lines · 14 tokens per session scan A 49777b0a07dc
eval-prompts is a command published in the GitHub repository timothywarner-org/claude-code (223 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 235 once invoked, about $0.0001 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.
Other commands, from other repositories
/spdd-reasons-canvas
Generate REASONS-Canvas structured prompts from business context without external template.
prompt-show
Display full details of a saved prompt by ID.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
design-prompt
Create a structured system prompt for an AI feature.
update-prompt
Update a prompt, system instruction, or agent definition by applying a research-backed prompt engineering technique.
prompt-create
Create a new prompt following ground rules.