One file. Your AI coding agent becomes a scientist.
Drop researcher.md into Claude Code, Cursor, or any agent that reads markdown. It will design experiments, test hypotheses, discard what fails, keep what works — 30+ experiments overnight while you sleep.
Branch: research/reduce-latency · Parent: #5 · Type: real
Hypothesis: KD-tree lookup should reduce p99 from 142ms to under 100ms Changes: swapped brute-force loop in
spatial_index.pywithscipy.spatial.KDTreeResult: p99 = 89ms (was 142ms baseline, 118ms best) — new best Status: keepInsight: The bottleneck was always the neighbor search, not the scoring. Experiment #3 (caching) was treating the symptom.
| # | branch | metric | status | description |
|---|---|---|---|---|
| 0 | research/reduce-latency | 142.00 | keep | Baseline measurement |
| 1 | research/reduce-latency | 139.20 | discard | Add response caching |
| 2 | research/reduce-latency | 141.50 | discard | Connection pooling tuning |
| 3 | research/reduce-latency | 135.80 | discard | LRU cache on hot path |
| 5 | research/reduce-latency | 118.00 | keep | Batch DB queries |
| 7 | research/reduce-latency | 89.00 | keep | KD-tree neighbor search |
Example is simulated. The skill works on any codebase — run it and share your real results.
Anything where you can measure or evaluate a result:
- API latency — p50, p99, throughput
- Test speed — suite runtime, parallelization strategies
- Bundle size — tree-shaking, code splitting, dependency swaps
- Prompt engineering — accuracy, cost, token usage
- Algorithm tuning — runtime complexity, memory usage
- Configuration optimization — DB settings, cache sizes, thread pools
The agent interviews you about what to optimize, sets up a lab on a git branch, then works autonomously — thinking, testing, reflecting — committing before every experiment, reverting on failure, logging everything. It forks branches to explore divergent approaches, detects when it's stuck, and keeps going until you stop it or it hits a target.
Generalizes autoresearch beyond ML: supports thought experiments, non-linear branching, qualitative metrics, convergence signals, and session resume.
All experiment history lives in an untracked .lab/ directory that survives all git operations — git manages code, .lab/ manages knowledge.
MIT
If this is useful, star the repo and share what you discovered.
Yggdrasil – your agent keeps forgetting your architecture, constraints, and past decisions. Yggdrasil gives your repository persistent semantic memory, so each task starts with the right context instead of another giant prompt.