erm-tune
erm-tune is an Audio, music & podcasts skill for AI agents, published by dougcalobrisi in the erm repository on GitHub. Skillspace rates erm-tune D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Diagnose and tune erm's output quality. Use when an erm run is imperfect or the user wants to adjust settings — fillers still audible, real words clipped, splices click or sound smeared/blurry, noise floor pumps or is audible, words run together, detection too aggressive or missing fillers, or questions about crossfade, pause spacing, denoise, room tone, models, or detection thresholds. For first-time install or a basic clean, use the erm skill instead.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 110
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
- 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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