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 | $0.00042 | $0.03313 |
| Opus 5.5 | $0.00017 | $0.01325 |
| Sonnet 5.5 | $0.00008 | $0.00663 |
| Haiku 4.5 | $0.00004 | $0.00331 |
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 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 — 281 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 the project's research 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 or MinTRL statistical tests
- Generating evaluation reports from fold results
No trial-count corrections (operator rule, 2026-10-15): never compute, report, rank by or cite the Deflated Sharpe Ratio, the minimum backtest length (MinBTL) or any other trial-count correction (FWER/FDR procedures such as Holm or Benjamini-Yekutieli, e-LOND and other online multiple testing). Judge parsimony on the model itself, guard selection with registration before runs and with unused data, and report each rule's own statistics (Sharpe, PSR, MinTRL). They were removed from this skill on purpose; do not re-add them. PBO/CSCV is not covered by the rule.
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 (4) | Final validation | psr, 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
15 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 8.4 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 9.5 KB
- references/ml-prediction-quality.md 8.3 KB
- references/risk-metrics.md 6.6 KB
- references/sharpe-formulas.md 9.1 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 21 KB runs code
- scripts/compute_metrics.test.ts 4.0 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.
- 7d ago Changed · +3 lines · +1 tokens per session c9ef99dd7c73
- 22d ago Changed db00841ae512
- 1mo 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 (1 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 3,313 once invoked, about $0.0002 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.
Other skills, from other repositories
monetization-strategy
Revenue models, pricing strategies, and business monetization. Use when designing pricing, choosing business models, or optimizing revenue for products and services.
payment-integration
Stripe, RevenueCat, subscription billing, webhooks, PCI compliance, and tax. Use when integrating payments or subscriptions, handling payment webhooks, or reviewing billing and PCI scope.
finance
Financial analysis expertise for financial modeling (DCF, LBO, M&A), valuation, financial statement analysis, capital allocation, treasury management, and corporate finance decisions. Use when building financial models, analyzing statements, or making investment decisions.
financial-scenario-planner
Stress-test financial plans across scenarios (bull/bear/base), sensitivity tables, and Monte Carlo-style analysis. Use when evaluating financial assumptions, modeling risk scenarios, or building scenario-based financial plans.
agent-payment-billing-specialist
Specialist subagent: Stripe integration, subscription billing, webhooks, PCI compliance, and tax handling specialist. Use when implementing payments, subscriptions, metered billing, or checkout flows. Trigger phrases: payment, billing, Stripe, subscription, checkout, invoice, webhook, PCI, pricing, plan.
investment-memo-generator
Investment memo creation combining financial analysis, document generation, and structured templates. Use when creating investment memos, pitch decks, deal summaries, or investment committee materials.