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 shihongDev/evalyn --skill evalyn-calibrategit clone --depth 1 https://github.com/shihongDev/evalynWrote 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/shihongdev/evalyn/evalyn-calibrate)<a href="https://agentmods.dev/skills/shihongdev/evalyn/evalyn-calibrate"><img src="https://agentmods.dev/badge/skills/shihongdev/evalyn/evalyn-calibrate/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/shihongdev/evalyn/evalyn-calibrate"><img src="https://agentmods.dev/badge/skills/shihongdev/evalyn/evalyn-calibrate.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 | $0.00028 | $0.01011 |
| Opus 5.5 | $0.00011 | $0.00404 |
| Sonnet 5.5 | $0.00006 | $0.00202 |
| Haiku 4.5 | $0.00003 | $0.00101 |
Grade A, and why
evalyn-calibrate 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 1mo 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evalyn-calibrate
Pre-flight
- Verify evaluation runs exist:
evalyn list-runs --limit 1
If no runs: "You need evaluation results first. Invoke evalyn-eval."
- Identify calibration targets:
evalyn analyze --run <latest-run-id>
Look for subjective metrics (type [llm]) with pass rates below 90%. Only subjective metrics can be calibrated - objective metrics are deterministic.
Step 1: Annotate Results
Run interactive annotation with per-metric mode for targeted feedback:
evalyn annotate --run-id <latest-run-id> --dataset <dataset-path> --per-metric
This is an interactive terminal session. The user will:
- See each item's input, output, and LLM judge results
- Agree or disagree with each metric's judgment
- Commands:
[y]es/pass,[n]o/fail,[s]kip,[v]iew full,[q]uit
Aim for 20-30+ annotations. Focus on disagreements. Annotations save immediately - quit and resume anytime.
Step 2: Run Calibration
Start with the basic optimizer (fast, single-shot analysis):
evalyn calibrate --metric-id <target-metric> --annotations <annotations-dir>
The --annotations flag points to the directory containing annotation files (created by evalyn annotate).
The output shows alignment metrics:
- Accuracy: overall agreement rate
- F1 Score: balanced precision/recall
- Cohen's Kappa: agreement adjusted for chance
Optimizer comparison
| Optimizer | Flag | Speed | Best For |
|---|---|---|---|
basic |
--optimizer basic |
Fast (1 API call) | First pass, small annotation sets |
ape |
--optimizer ape |
Medium | Exploring many prompt variants |
opro |
--optimizer opro |
Medium | Iterative refinement |
gepa-native |
--optimizer gepa-native |
Slow | Best quality, built-in token tracking |
gepa |
--optimizer gepa |
Slow | Evolutionary (requires pip install gepa) |
evoprompt |
--optimizer evoprompt |
Medium | Evolutionary mutation/crossover of prompts |
textgrad |
--optimizer textgrad |
Medium | Critique-revise gradient descent on text |
miprov2 |
--optimizer miprov2 |
Medium | Instruction + few-shot demo co-optimization |
promptbreeder |
--optimizer promptbreeder |
Slow | Self-referential evolutionary prompt search |
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.
- 1mo ago First seen · 117 lines · 28 tokens per session scan A 7395913b73a0
evalyn-calibrate is a skill published in the GitHub repository shihongDev/evalyn (258 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 1,011 once invoked, about $0.0001 per session on Opus 5.5. 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.
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