Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/terrylica/cc-skillsnpx agentmods add skills/terrylica/cc-skills/opendeviation-eval-metricsWrote 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/opendeviation-eval-metrics)<a href="https://agentmods.dev/skills/terrylica/cc-skills/opendeviation-eval-metrics"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/opendeviation-eval-metrics/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/opendeviation-eval-metrics"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/opendeviation-eval-metrics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Rogue Agent · line 275 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00041 | $0.03190 |
| Opus 5 | $0.00020 | $0.01595 |
| Sonnet 5 | $0.00008 | $0.00638 |
| Haiku 4.5 | $0.00004 | $0.00319 |
Grade A, and why
opendeviation-eval-metrics 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 6d 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 — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open Deviation Bar Evaluation Metrics
Machine-readable reference + computation scripts for state-of-the-art metrics evaluating open deviation bar (ODB, brim-to-brim price-based sampling) data.
Cross-reference: Project-level experiment catalogue at signal-archaeology skill in opendeviationbar-patterns repo — contains 10 BHR-validated experiments with auditable SQL.
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:
- Evaluating ML model performance on open deviation bar data
- Computing Sharpe ratios with non-IID bar sequences
- Running Walk-Forward Optimization metric analysis
- Calculating PSR, DSR, or MinTRL statistical tests
- Generating evaluation reports from fold results
Quick Start
# Compute metrics from predictions + actuals
python scripts/compute_metrics.py --predictions preds.npy --actuals actuals.npy --timestamps ts.npy
# Generate full evaluation report
python scripts/generate_report.py --results folds.jsonl --output report.md
Metric Tiers
| Tier | Purpose | Metrics | Compute |
|---|---|---|---|
| Primary (5) | Research decisions | weekly_sharpe, hit_rate, cumulative_pnl, n_bars, positive_sharpe_rate | Per-fold + aggregate |
| Secondary/Risk (5) | Additional context | max_drawdown, bar_sharpe, return_per_bar, profit_factor, cv_fold_returns | Per-fold |
| ML Quality (3) | Prediction health | ic, prediction_autocorr, is_collapsed | Per-fold |
| Diagnostic (5) | Final validation | psr, dsr, autocorr_lag1, effective_n, binomial_pvalue | Aggregate only |
| Extended Risk (5) | Deep risk analysis | var_95, cvar_95, omega_ratio, sortino_ratio, ulcer_index | Per-fold (optional) |
What ships with it
14 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.
- references/anti-patterns.md 8.5 KB
- references/beyond-hit-rate.md 8.8 KB
- references/crypto-markets.md 9.9 KB
- references/evolution-log.md 683 B
- references/metrics-schema.md 9.7 KB
- references/ml-prediction-quality.md 8.3 KB
- references/risk-metrics.md 6.6 KB
- references/sharpe-formulas.md 10 KB
- references/sota-2025-2026.md 19 KB
- references/structured-logging.md 8.0 KB
- references/temporal-aggregation.md 3.8 KB
- references/worked-examples.md 14 KB
- requirements.txt 500 B
- scripts/compute_metrics.py 20 KB runs code
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.
- 6d ago First seen · 278 lines · 41 tokens per session scan A 416093963190
opendeviation-eval-metrics is a skill published in the GitHub repository terrylica/cc-skills (72 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 3,190 once invoked, about $0.0002 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 2026-09-05.
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