ai-agents-research-methodology
ai-agents-research-methodology is an Agent behaviour & coding discipline skill for AI agents, published by rjmurillo in the ai-agents repository on GitHub. Skillspace rates ai-agents-research-methodology B (81/100) and it passed the safety scan; the rating is based on static analysis only.
How a hunch becomes an accepted result in this repo. Covers the evidence bar, hypothesis-predicts-numbers discipline, and the idea lifecycle from contradiction log through probe, eval baseline, ADR debate, calibrated gate, and guard-maturity monitoring. Use when you say `how do I prove this idea`, `run the idea lifecycle`, `what is the evidence bar`. Do NOT use for the open research programs (use ai-agents-research-frontier) or probe recipe depth (use ai-agents-empirical-probe-toolkit).
- Grade
- B
- Quality
- 81 / 100
- Safety
- Passed
- GitHub stars
- 38
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
- 90 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- MIT
- 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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