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 leecyno1/boutique-skills --skill alphagbm-marks-cyclegit clone --depth 1 https://github.com/leecyno1/boutique-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/leecyno1/boutique-skills/alphagbm-marks-cycle)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/alphagbm-marks-cycle"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-marks-cycle/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/leecyno1/boutique-skills/alphagbm-marks-cycle"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-marks-cycle.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00172 | $0.01124 |
| Opus 5 | $0.00086 | $0.00562 |
| Sonnet 5 | $0.00034 | $0.00225 |
| Haiku 4.5 | $0.00017 | $0.00112 |
Grade A, and why
alphagbm-marks-cycle 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 11d 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.
This is a copy
100% identical to alphagbm-marks-cycle — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaGBM Howard Marks Cycle
"Cycles are real — the shape just isn't predictable." Howard Marks's framework rejects forecasting and replaces it with cycle-position awareness: offense when others are pessimistic, defense when others are optimistic.
This skill gives you the one number Marks's entire philosophy implies: where are we right now.
The Cycle Score
Each signal is mapped to its own cycle component 0-100, then weighted:
| Signal | Weight | Interpretation |
|---|---|---|
| VIX | 40% | Low VIX → complacency → late cycle (high score). High VIX → fear → early cycle (low score) |
| IV Rank (SPY) | 25% | High IV rank → fear → early cycle |
| Put/Call ratio | 20% | Low P/C → complacent → late cycle |
| Valuation percentile | 15% | Higher PE percentile → later cycle |
Weights renormalize when data points are missing (e.g., P/C not available).
Posture Bands
- 0-24 →
OFFENSE_HARD— extreme fear is opportunity. Buy aggressively. - 25-39 →
OFFENSE— add, sell vol (short premium). - 40-59 →
NEUTRAL— maintain positions, watch for shifts. - 60-74 →
DEFENSE— don't add, brace for volatility. - 75-100 →
DEFENSE_HARD— trim, buy protection (long puts / collars).
Why This Is a Separate Skill
alphagbm-vix-status gives just a VIX tier. alphagbm-market-sentiment gives a
sentiment dashboard. This skill is the one-call Marks-specific read:
"given everything I know about sentiment + valuation, what's the posture?"
How to Use
Input: none (market-level, no ticker)
Output:
cycle_score: integer 0-100posture: one ofOFFENSE_HARD / OFFENSE / NEUTRAL / DEFENSE / DEFENSE_HARDposture_zh,posture_en: natural-language prescriptioncomponents: per-signal{value, cycle_component}breakdown
Example Queries
where are we in the cycle right now→ headline cycle number + postureshould I be playing offense or defense→ posture directly answersHoward Marks read on the market→ same data, framed as Marks wouldis this a buying cycle→ cycle < 30 → yes; cycle > 60 → nocurrent sentiment across VIX and IV rank→ components breakdown
What ships with it
17 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.
- LICENSE 1.0 KB
- mock-data/AAPL.json 6.7 KB
- mock-data/buffett-analysis/example-ko.json 1.9 KB
- mock-data/fear-score/example-calm.json 661 B
- mock-data/fear-score/example-signal-triggered.json 663 B
- mock-data/hedge-advisor/example-gain-protection.json 1.5 KB
- mock-data/marks-cycle/example-neutral.json 366 B
- mock-data/META.json 8.8 KB
- mock-data/NVDA.json 8.4 KB
- mock-data/SPY.json 7.5 KB
- mock-data/take-profit/example-leveraged-etf.json 1.1 KB
- mock-data/tepper-signal/example-armed.json 589 B
- mock-data/tepper-signal/example-cold.json 488 B
- mock-data/TSLA.json 9.4 KB
- mock-data/vix-status/example-extreme-fear.json 528 B
- mock-data/vix-status/example-sweet-spot.json 506 B
- SOURCE.txt 450 B
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.
- 11d ago First seen · 112 lines · 172 tokens per session scan A a85c036c134f
alphagbm-marks-cycle is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed 20d ago), licensed MIT. It adds 172 tokens to every session and 1,124 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to alphagbm-marks-cycle, differing in 0 lines, and is treated as a copy.
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