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SubliminalCodingSubliminalCoding/bilbroswagginz-skills/skills/qwen-perf-max/SKILL.md

qwen-perf-max

Skill0 starsChanged 47 days agoMITView on GitHub

Local-Qwen performance pass (token-free). Mechanical form of /perf-max for a small local model: only on settled code with a real workload, profile to a scored hotspot list, optimize the top one at a time, prove behavior is unchanged AND measure the win (revert if no measurable gain or behavior changed), re-profile until the wins dry up. Use when you want /perf-max's job done on a local model with no API tokens: speeding up a project.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If unsure whether to run this → ask the user (DECISIONS.md).

    line 30
  2. Distinctive:

    Never keep a "faster" change that altered behavior or reds a test (fast-and-wrong = wrong).

    line 54
  3. Distinctive:

    Never keep a change with no measured win (that's just added complexity).

    line 55

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

qwen-perf-max (local performance)

The Qwen-run version of /perf-max. Profile first, always. Never optimize by guess.

QWEN LOCAL RULES (full text: ../qwen-forge/references/QWEN-MODE.md)

Requires qwen-forge installed alongside this skill (it holds the shared rules).

  • Builder, not architect. ONE change at a time; build → tests → lint; green → commit; red → revert, one retry, else STOP. Never weaken tests.
  • Unclear / judgment / outward → DECISIONS.md + STOP.
  • Local endpoint required: a local OpenAI-compatible endpoint (vLLM, SGLang, llama.cpp or Ollama) serving a capable Qwen-class coding model. ONE model loaded, work SEQUENTIALLY, leave memory headroom on unified-memory machines.

Gate (do NOT start unless both are true)

  1. The Intent actually cares about speed.
  2. Features are settled (not still being built). Optimizing churning code is wasted.

If unsure whether to run this → ask the user (DECISIONS.md).

Steps

1. WORKLOAD. There must be a realistic way to measure (a benchmark / repeatable run). If
   there isn't one → STOP and make a small one first; you cannot optimize what you can't
   measure.

2. PROFILE. Run hotspot-scout against the workload → a SCORED hotspot
   list (CPU / memory / I/O). No profile output → do NOT optimize.

3. OPTIMIZE. The TOP hotspot, ONE change at a time:
     - make the change.
     - tests green (behavior UNCHANGED); golden/metamorphic test if outputs matter.
     - measure before/after (the number from step 1's workload).
     - NO measurable win, OR behavior changed → REVERT. Otherwise commit.

4. RE-PROFILE. The bottleneck moves after each fix. Re-run step 2. Stop when a pass finds
   no material win left. Report the before/after numbers.

Rules

  • Never keep a "faster" change that altered behavior or reds a test (fast-and-wrong = wrong).
  • Never keep a change with no measured win (that's just added complexity).
  • Profile every round; do not optimize the same spot twice without re-measuring.

Distilled from /perf-max. Its ROUTING.md holds the full version for a Claude/human driver.

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