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BF options-spread-conviction-engine

Multi-regime options spread analysis engine with quantitative rigor. Features regime detection (VIX-based), GARCH volatility forecasting, drawdown-constrained Kelly position sizing, and walk-forward backtesting. Scores vertical spreads (bull put, bear call, bull call, bear put) and multi-leg strategies (iron condors, butterflies, calendar spreads) using Ichimoku, RSI, MACD, Bollinger Bands, and IV term structure analysis.

modbender/skill-library-mcp Agent Skills author: modbender MIT 33 files · 1 script body ≈ 5 627 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Multi-regime options spread analysis engine with quantitative rigor.

As a process F 44/100 · Will not run — References files that are not bundled: scripts/conviction-engine

AnalyzerData and analyticsPersonal productivityResearchtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
81/100
safety, quality, tests
Safety 60%
100
Quality 40%
53
Run on models
none yet
Process rating
F
44/100
Will not run
References files that are not bundled: scripts/conviction-engine
Tools and files w 18
0
Result and completion w 14
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 28. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5627 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: scripts/conviction-engine

Process rating: all ten parameters 44/100

Will not run. References files that are not bundled: scripts/conviction-engine
  • 0Tools and files. 1 referenced file(s) missing: scripts/conviction-engine
  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5627 tokens
  • 100Steps. 135 steps
  • 100Consistency. Name and required fields are in place
  • low 19 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -36 of 20 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 425: enough signal without eating the budget
  • +4Structure: 55 headings
  • +3Step-by-step instructions: 135 items
  • +4Has examples (25 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.