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BF alphagbm-earnings-crush

Full earnings-season IV analysis: historical crush, implied move forecast, IV Rank strategy tag, and a priced Iron Condor quote ready to trade. Triggers: "earnings crush AAPL", "NVDA IV before earnings", "implied move MSFT", "iron condor for META", "IV rank AAPL earnings", "earnings play TSLA", "should I short premium before AMZN earnings", "post-earnings IV drop", "straddle before earnings", "pre-earnings strategy"

ClawHub Agent Skills author: Clement Gu v1.0.0 MIT-0 2 files body ≈ 957 tokens Open the sourceclawhub.ai↗ analyzed 20 h ago

Full earnings-season IV analysis: historical crush, implied move forecast, IV Rank strategy tag, and a priced Iron Condor quote ready to trade.

As a process F 33/100 · Will not run — References files that are not bundled: ../alphagbm-iv-rank/, ../alphagbm-options-strategy/, ../alphagbm-vol-surface/

AnalyzerData and analyticstype 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
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
F
33/100
Will not run
References files that are not bundled: ../alphagbm-iv-rank/, ../alphagbm-options-strategy/, ../alphagbm-vol-surface/
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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 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: 2. 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 missing-ref reference to a missing file: ../alphagbm-iv-rank/
  • warning missing-ref reference to a missing file: ../alphagbm-options-strategy/
  • warning missing-ref reference to a missing file: ../alphagbm-vol-surface/
  • note frontmatter-key unknown frontmatter key "globs"

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: ../alphagbm-iv-rank/, ../alphagbm-options-strategy/, ../alphagbm-vol-surface/
  • 0Tools and files. 3 referenced file(s) missing: ../alphagbm-iv-rank/, ../alphagbm-options-strategy/, ../alphagbm-vol-surface/
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 100Steps. 26 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 957 tokens

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 8 example trigger phrases
  • +3Description length 419: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: clean
This skill is a plain markdown guide for earnings options analysis and does not contain code, persistence, credential access, or hidden execution behavior.
LLM: benign (high) · VirusTotal: · 1 Jul 2026