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 zigzag-pattern-classifiergit 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/zigzag-pattern-classifier)<a href="https://agentmods.dev/skills/terrylica/cc-skills/zigzag-pattern-classifier"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/zigzag-pattern-classifier.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00038 | $0.03631 |
| Opus 5 | $0.00019 | $0.01816 |
| Sonnet 5 | $0.00008 | $0.00726 |
| Haiku 4.5 | $0.00004 | $0.00363 |
Grade A, and why
zigzag-pattern-classifier 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 2d 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ZigZag Swing Pattern Classifier
Complete taxonomy for classifying ZigZag swing patterns by structure and market regime. Every confirmed ZigZag sequence maps to exactly one variant — no gaps, no overlaps.
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
- Classifying a confirmed L₀→H₁→L₂ (two-pivot) or L₀→H₁→L₂→H₃ (three-pivot) swing
- Looking up market regime implications of a specific pattern
- Answering "how many distinct patterns exist?" and proving exhaustiveness
- Implementing pattern labeling in code (Rust
qtacrate, Python pipelines) - Understanding the epsilon tolerance band that defines "equal"
- Applying Freedman-Diaconis binning for sub-classification depth
Notation (Single Source of Truth)
| Symbol | Definition | Example |
|---|---|---|
| L₀ | Initial low (first confirmed pivot) | 1.0800 |
| H₁ | Swing high (reversal peak) | 1.0850 |
| L₂ | Second low (retracement) | 1.0810 |
| H₃ | Second high (three-pivot only) | 1.0870 |
| W | Swing magnitude: H₁ − L₀ | 0.0050 |
| z | Normalized retracement: (L₂ − L₀) / (H₁ − L₀) | 0.20 |
| o | Volatility-normalized overshoot: (L₀ − L₂) / ATR₁₄ | 0.35 |
| ε | Tolerance band for "equal" classification | 5 pips |
| τ | ZigZag reversal threshold | 6.6 pips |
Price Level Comparisons
| Code | Meaning | Condition |
|---|---|---|
| HL | Higher Low | L₂ > L₀ + ε |
| EL | Equal Low | |L₂ − L₀| ≤ ε |
| LL | Lower Low | L₂ < L₀ − ε |
| HH | Higher High | H₃ > H₁ + ε |
| EH | Equal High | |H₃ − H₁| ≤ ε |
| LH | Lower High | H₃ < H₁ − ε |
What ships with it
7 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.
- 2d ago First seen · 285 lines · 38 tokens per session scan A 8b7bb9ea4210
zigzag-pattern-classifier is a skill published in the GitHub repository terrylica/cc-skills (62 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 3,631 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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