symbolic-check
symbolic-check is a Physics, mathematics & simulation skill for AI agents, published by flonat in the flonat-research repository on GitHub. Skillspace rates symbolic-check D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Use when you need to symbolically verify a SELF-AUTHORED algebra step, derivative, limit, comparative-static sign, or closed-form identity using a CAS (sympy) — proving or refuting it, not just stress-testing. R2 of the verification spectrum: unlike numerical-check (which only falsifies), symbolic verification can positively PROVE a manipulation. Triggers: symbolic-check, 'verify this algebra', 'is this derivative right', 'check the sign of a comparative static', 'does this closed form equal the original', 'verify the limit'. NOT for a full formal theorem (use lean-check), a probabilistic/distributional claim over a parameter space (use numerical-check), or empirical replication (use cross-language-check).
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 118
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
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- 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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