adversarial-research-analyst

adversarial-research-analyst is a Structured reasoning & decision frameworks skill for AI agents, published by sou350121 in the VLA-Handbook repository on GitHub. Skillspace rates adversarial-research-analyst D (66/100) and it passed the safety scan; the rating is based on static analysis only.

Adversarial research analysis framework that uses structured Bull/Bear/Arbiter debates to help users make better research judgments. Maintains a belief graph as backend engine, applies statistical calibration discipline, tracks phase transitions, and detects biases. MANDATORY TRIGGERS: Use this skill whenever the user asks to analyze a research paper, evaluate a research direction, make a strategic research decision, assess technology trends, review academic papers, or asks "what should I work on / invest in / bet on" in a research context. Also trigger when the user mentions "paper review", "research direction", "trend analysis", "technology forecast", "belief update", or wants structured pro/con analysis of any technical topic. Even casual requests like "what do you think about this paper" or "is X going to be important" should trigger this skill.

Grade
D
Quality
66 / 100
Safety
Passed
GitHub stars
415

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

Our review

Architecture
84 / 100
Maturity
Structured: ships references or scripts
Security scan
No issues found
License
CC-BY-4.0
Adoption
Adopted by 0 other repositories, in 0 collections

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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