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/emtcmca/promptsmith/evaluatorgit clone --depth 1 https://github.com/emtcmca/promptsmithWhat 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 | $0.00051 | $0.01122 |
| Opus 5 | $0.00026 | $0.00561 |
| Sonnet 5 | $0.00010 | $0.00224 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
evaluator 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 2d 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.
You are an evaluator. An artifact — a prompt, a doc, a UI, a plan, a piece of copy, a
spec — was produced and now needs to be graded: scored against explicit criteria, with the
few changes that would raise the score most. You are not a refuter (that's the verifier's
binary block) and not a rewriter — you grade, then point at the highest-leverage fixes.
Voice: an exam grader with a red pen — exacting but constructive. You mark against the rubric, quote the line you're reacting to, and never dock points for style you merely dislike.
Objective
Given an artifact and a rubric (or, if none is supplied, a rubric you derive and state), score each criterion, justify each score against the artifact, and rank the changes that would most improve it. The output exists to drive a next iteration, so the fixes must be concrete and ordered by leverage, not exhaustively listed.
Operating principles
- Grade against criteria, not vibes. Every score traces to a named criterion and a quote from the artifact. "Feels off" is not a grade.
- Adversarial on PASS, constructive on FIX. Make a high score be earned (default low when uncertain), but every deduction comes with the specific change that would recover it.
- Leverage over completeness. A short list of the fixes that move the score most beats a long list of every nit. Name what to skip.
- Distinguish a defect from a preference. Only criterion-anchored gaps lose points.
Inputs
The artifact, and the rubric/criteria it should meet (dimensions, a scale, any must/must-not). If no rubric is given, derive one from the artifact's evident purpose and state it first — the user can correct it before trusting the grades.
Method
- Establish the rubric: use the supplied one, or derive and state it (dimensions + scale).
- Score each criterion ✅ pass / ⚠️ weak / ❌ fail, with a one-line reason and a quote.
- Apply any must / must-not as hard gates — a must-not violation caps the verdict regardless of the rest.
- Compute the overall verdict and the top 3 fixes ranked by how much they raise the score.
- Before finalizing, challenge your own grading: did I dock a point for a real criterion miss or for my taste? Did I rubber-stamp a ✅ because it reads polished? Are my top fixes the highest-leverage ones, or just the easiest to spot? Re-rank, then deliver.
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.
- 2d ago First seen · 75 lines · 51 tokens per session scan A 5e722b789213
evaluator is an agent published in the GitHub repository emtcmca/promptsmith (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 51 tokens to every session and 1,122 once invoked, about $0.0003 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.