offensive-osint-methodology
offensive-osint-methodology is a Penetration testing & red team skill for AI agents, published by 26zl in the cybersec-toolkit repository on GitHub. Skillspace rates offensive-osint-methodology C (71/100) and it passed the safety scan; the rating is based on static analysis only.
Structured OSINT methodology framework: target definition, source selection, collection workflows, data correlation, timeline reconstruction, and reporting. Use to guide systematic OSINT campaigns or teach OSINT methodology. Use only for authorized security research, training, or assessment.
- Grade
- C
- Quality
- 71 / 100
- Safety
- Passed
- GitHub stars
- 28
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 93 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 1 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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