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 terrylica/cc-skills --skill evolutionary-metric-rankinggit clone --depth 1 https://github.com/terrylica/cc-skillsWrote 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/terrylica/cc-skills/evolutionary-metric-ranking)<a href="https://agentmods.dev/skills/terrylica/cc-skills/evolutionary-metric-ranking"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/evolutionary-metric-ranking/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/terrylica/cc-skills/evolutionary-metric-ranking"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/evolutionary-metric-ranking.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 | $0.00025 | $0.04853 |
| Opus 5.5 | $0.00010 | $0.01941 |
| Sonnet 5.5 | $0.00005 | $0.00971 |
| Haiku 4.5 | $0.00003 | $0.00485 |
Grade A, and why
evolutionary-metric-ranking 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 7d 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 — 506 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evolutionary Metric Ranking
Methodology for systematically zooming into high-quality configurations across multiple evaluation metrics using per-metric percentile cutoffs, intersection-based filtering, and evolutionary optimization. Domain-agnostic principles with quantitative trading case studies.
Companion skills: rangebar-eval-metrics (metric definitions) | adaptive-wfo-epoch (WFO integration) | backtesting-py-oracle (SQL validation)
Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.
When to Use This Skill
Use this skill when:
- Ranking and filtering configs/strategies/models across multiple quality metrics
- Searching for optimal per-metric thresholds that select the best subset
- Identifying which metrics are binding constraints vs inert dimensions
- Running multi-objective optimization (Optuna TPE / NSGA-II) over filter parameters
- Performing forensic analysis on optimization results (universal champions, feature themes)
- Designing a metric registry for pluggable evaluation systems
Core Principles
P1 - Percentile Ranks, Not Raw Values
Raw metric values live on incompatible scales (Kelly in [-1,1], trade count in [50, 5000], Omega in [0.8, 2.0]). Percentile ranking normalizes every metric to [0, 100], making cross-metric comparison meaningful.
Rule: scipy.stats.rankdata(method='average') scaled to [0, 100]
None/NaN/Inf -> percentile 0 (worst)
"Lower is better" metrics -> negate before ranking (100 = best)
Why average ties: Tied values receive the mean of the ranks they would span. This prevents artificial discrimination between genuinely identical values.
P2 - Independent Per-Metric Cutoffs
Each metric gets its own independently-tunable cutoff. cutoff=20 means "only configs in the top 20% survive this filter." This creates a 12-dimensional (or N-dimensional) search space where each axis controls one quality dimension.
What ships with it
4 files 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.
- 7d ago Changed · -1 lines a15d8618c5f7
- 28d ago Changed 6fb99bd05275
- 1mo ago Changed 07cb22f07f3d
- 1mo ago First seen · 507 lines · 25 tokens per session scan A 7cf214c023ee
evolutionary-metric-ranking is a skill published in the GitHub repository terrylica/cc-skills (1 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 4,853 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 2026-09-05.
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