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-analyzegit 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-analyze)<a href="https://agentmods.dev/skills/shihongdev/evalyn/evalyn-analyze"><img src="https://agentmods.dev/badge/skills/shihongdev/evalyn/evalyn-analyze/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-analyze"><img src="https://agentmods.dev/badge/skills/shihongdev/evalyn/evalyn-analyze.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 | $0.00023 | $0.00720 |
| Opus 5.5 | $0.00009 | $0.00288 |
| Sonnet 5.5 | $0.00005 | $0.00144 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
evalyn-analyze 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evalyn-analyze
Overview
Analyze evaluation results progressively: summary, insights, failure clustering, and trend analysis. Interpret findings and recommend next actions based on pass rates.
Pre-flight
Verify evaluation runs exist:
evalyn list-runs --limit 3
If no runs: "You need to run an evaluation first. Invoke evalyn-eval."
Identify the latest run ID from the output.
Step 1: Metric Summary
evalyn analyze --run <run-id>
This shows:
- Per-metric pass rates and average scores
- Key findings (highest/lowest performing metrics)
- Overall health rating (GOOD/MODERATE/POOR)
You can also use short IDs (first 8 characters of run ID).
Step 2: Deep Insights
evalyn insights --run <run-id>
Provides diagnostic and prescriptive analysis:
- Metric correlations (which metrics move together)
- Anomaly detection
- Actionable recommendations
Step 3: Compare and Trend
If multiple runs exist (check evalyn list-runs output):
evalyn compare --run1 <previous-run-id> --run2 <latest-run-id>
For longer history across all runs in a dataset:
evalyn trend --project <project-name>
Note: use --run1 and --run2 flags for compare, not positional arguments.
Step 4: Investigate Failures
If any metric has pass rate below 90%:
evalyn cluster-failures --run-id <run-id>
This clusters failed items by failure reason, revealing patterns (e.g., "all failures involve long inputs" or "failures cluster around a specific topic").
Step 5: Interpret and Recommend
Based on the results, recommend next action:
| Overall Pass Rate | Interpretation | Recommendation |
|---|---|---|
| Above 95% | Agent performing well | Consider evalyn simulate --dataset <path> --modes similar,outlier for edge case testing. Export report: evalyn export --run <id> --format html |
| 80-95% | Moderate issues | Review failing items. Could be agent issues OR judge misalignment. Consider invoking evalyn-calibrate to verify judges are accurate. |
| Below 80% | Significant issues | Invoke evalyn-calibrate to annotate items and check if judges agree with human expectations. Fix agent if judges are correct. |
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 · 101 lines · 23 tokens per session scan A bcd62c163562
evalyn-analyze is a skill published in the GitHub repository shihongDev/evalyn (258 stars, last pushed 4mo ago), licensed MIT. It adds 23 tokens to every session and 720 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…