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BF alphagbm-compare

Side-by-side comparison of 2-5 stocks or options across GBM Five Pillars scores, options metrics, technicals, and valuations. Identifies the winner by category. Triggers: "compare AAPL vs MSFT", "NVDA or AMD", "which is cheaper TSLA or META options", "tech stock comparison", "side by side", "versus", "which is better", "compare options", "cheapest IV", "best value stock"

ClawHub Agent Skills author: Clement Gu v1.0.0 MIT-0 2 files body ≈ 663 tokens Open the sourceclawhub.ai↗ analyzed 20 h ago

Side-by-side comparison of 2-5 stocks or options across GBM Five Pillars scores, options metrics, technicals, and valuations.

As a process F 35/100 · Will not run — References files that are not bundled: ../alphagbm-stock-analysis/, ../alphagbm-options-score/, ../alphagbm-iv-rank/

ProcedureData and analyticsFinancetype 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
35/100
Will not run
References files that are not bundled: ../alphagbm-stock-analysis/, ../alphagbm-options-score/, ../alphagbm-iv-rank/
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: 0. 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-stock-analysis/
  • warning missing-ref reference to a missing file: ../alphagbm-options-score/
  • warning missing-ref reference to a missing file: ../alphagbm-iv-rank/
  • note frontmatter-key unknown frontmatter key "globs"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: ../alphagbm-stock-analysis/, ../alphagbm-options-score/, ../alphagbm-iv-rank/
  • 0Tools and files. 3 referenced file(s) missing: ../alphagbm-stock-analysis/, ../alphagbm-options-score/, ../alphagbm-iv-rank/
  • 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
  • 100Steps. 16 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 663 tokens
  • 100Running it twice. No mutating operations

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 10 example trigger phrases
  • +3Description length 373: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (1 code blocks)

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

External checks

ClawHub: clean
This is a simple stock/options comparison prompt skill with no executable code, persistence, credentials, or hidden data access.
LLM: benign (high) · VirusTotal: · 1 Jul 2026