fastopt

Solid

Use when optimizing suspected hot paths without waiting for benchmarks. Not for estimated-floor optimization: use fastopt-extreme. Not for measured-floor rebuild campaigns: use extremely-optimize.

AI & Automation 38 stars 0 forks Updated yesterday Apache-2.0

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Skill Content

# Fastopt ## Contract | Field | Bound contract | |---|---| | Trigger | User wants to optimize suspected hot paths without waiting for benchmarks, labeling claims as hypotheses. | | Authority | Read-only. No file, VCS, credential, paid, published, deployed, or remote mutation. | | Side effect | Chat output only: hypotheses and benchmark designs before any mutation or performance claim. | | Done | Hypotheses and benchmark designs for suspected hot paths are emitted; no mutation or speed claim made. | ## Inputs Required: one or more suspected hot paths named by the user (file, function, or call site) and the workload or input shape that makes each path hot. Optional: prior measurements, profiling traces, or environment constraints the user supplies. Absent measurements are treated as unmeasured, not as evidence of slowness. ## Procedure 1. For each named hot path, read the source and surrounding call graph read-only; record the inputs, allocations, loops, and branches that plausibly dominate cost. Done when: every named path is read and its cost-plausible elements recorded. 2. Label every cost observation a hypothesis, not a finding: state the assumed workload, the assumed dominant operation, and the assumed magnitude rank, with no speed claim. Done when: every observation carries its hypothesis label with workload, dominant operation, and magnitude rank. 3. For each hypothesis, design a benchmark that would confirm or refute it: name the metric, the baseline, the variant...

Details

Author
OutlineDriven
Repository
OutlineDriven/odin-claude-plugin
Created
10 months ago
Last Updated
yesterday
Language
Python
License
Apache-2.0

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