donner-fuzzing
donner-fuzzing is an Application security skill for AI agents, published by jwmcglynn in the donner repository on GitHub. Skillspace rates donner-fuzzing D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Run, add, and triage Donner's libFuzzer fuzzers: the donner_cc_fuzzer three-target pattern, --config=asan-fuzzer, the manual deep-fuzz loop, corpus lifecycle, and the continuous-fuzzing harness in tools/fuzzing/. Use when a parser change warrants fuzz validation, when reproducing or fixing a crash-* / leak-* / timeout-* artifact or a fuzzer-filed GitHub issue, when adding a fuzzer for a new parser, or when managing fuzz corpora (manage_corpus.py, *_corpus dirs).
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 29
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
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- ISC
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Category is a classifier prediction; no model has read this skill 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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