ade-autoresearch
ade-autoresearch is a Runtime diagnostics & profiling skill for AI agents, published by arul28 in the ADE repository on GitHub. Skillspace rates ade-autoresearch D (63/100) and it passed the safety scan; the rating is based on static analysis only.
Iteratively optimize an ADE tab's CPU/memory/IPC/render performance. Drives the real UI, builds tab-specific probes from the visible product and perf-pass repo, identifies bottlenecks from JSONL metrics, makes ONE targeted code change per iteration, gates on tests + smoke, keeps wins on a branch, and distills patterns into per-tab perf skills. Invoke when the user says "optimize <tab>", "autoresearch <tab>", or "perf pass on <tab>". Drives ADE pointed at the perf-pass throwaway repo; full liberty inside that repo. Uses Codex/GPT models for in-ADE AI activity unless the run explicitly opts into another configured provider for comparison.
- Grade
- D
- Quality
- 63 / 100
- Safety
- Passed
- GitHub stars
- 27
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
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- AGPL-3.0
- 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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