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 RudyCity/superagent --skill evaluation-methodologygit clone --depth 1 https://github.com/RudyCity/superagentWrote 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/rudycity/superagent/evaluation-methodology)<a href="https://agentmods.dev/skills/rudycity/superagent/evaluation-methodology"><img src="https://agentmods.dev/badge/skills/rudycity/superagent/evaluation-methodology.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 213 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00077 | $0.05444 |
| Opus 5 | $0.00039 | $0.02722 |
| Sonnet 5 | $0.00015 | $0.01089 |
| Haiku 4.5 | $0.00008 | $0.00544 |
Grade A, and why
evaluation-methodology 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 4d 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 — 551 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluation Methodology
This document is the authoritative reference for how PluginEval measures plugin and skill quality. It covers the three evaluation layers, all ten scoring dimensions, the composite formula, badge thresholds, anti-pattern flags, Elo ranking, and actionable improvement tips.
Related: Full rubric anchors
The Three Evaluation Layers
PluginEval stacks three complementary layers. Each layer produces a score between 0.0 and 1.0 for each applicable dimension, and later layers override or blend with earlier ones according to per-dimension blend weights.
Layer 1 — Static Analysis
Speed: < 2 seconds. No LLM calls. Deterministic.
The static analyzer (layers/static.py) runs six sub-checks directly against the parsed SKILL.md:
| Sub-check | What it measures |
|---|---|
frontmatter_quality |
Name presence, description length, trigger-phrase quality |
orchestration_wiring |
Output/input documentation, code block count, orchestrator anti-pattern |
progressive_disclosure |
Line count vs. sweet-spot (200–600 lines), references/ and assets/ bonuses |
structural_completeness |
Heading density, code blocks, examples section, troubleshooting section |
token_efficiency |
MUST/NEVER/ALWAYS density, duplicate-line repetition ratio |
ecosystem_coherence |
Cross-references to other skills/agents, "related"/"see also" mentions |
These six sub-checks feed directly into six of the ten final dimensions (via STATIC_TO_DIMENSION
mapping). The remaining four dimensions — output_quality, scope_calibration,
robustness, and part of triggering_accuracy — receive no static contribution and rely
entirely on Layer 2 and/or Layer 3.
Anti-pattern penalty is applied multiplicatively to the Layer 1 score:
penalty = max(0.5, 1.0 − 0.05 × anti_pattern_count)
Each additional detected anti-pattern reduces the score by 5%, flooring at 50%.
Layer 2 — LLM Judge
Speed: 30–90 seconds. One or more LLM calls (Sonnet by default). Non-deterministic.
What ships with it
1 file 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.
- 4d ago First seen · 551 lines · 77 tokens per session scan A 1fe24ef30536
evaluation-methodology is a skill published in the GitHub repository RudyCity/superagent (21 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 5,444 once invoked, about $0.0004 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 2026-09-03.
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