bench
Skill1 starsChanged 31 days agoMITView on GitHub
Instructions for the upstream routing benchmark and its fixed decision corpus. Use to inspect estimated routing costs against a declared baseline, or to plan reproducible evidence. Decision estimates are not measured provider bills, production savings or validation of this instruction catalog.
10 other projects use a version of this, 1 of them edited.
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Most recently taken up by
- MrHashMe/efficiency-playbooktheir own version, first seen Nov 8, 2026
- ddalcu/mlx-servetheir own version, first seen Nov 1, 2026
- CorvusWing/CorvusWingtheir own version, first seen Oct 25, 2026
- a-ghorbani/pocketpal-dev-teamtheir own version, first seen Oct 18, 2026
- leo1852098393-blip/mxopstheir own version, first seen Oct 13, 2026
- FalkorDB/FalkorDBtheir own version, first seen Oct 8, 2026
Other versions10 projects use a version of this.
Word for word · 0
None found.
Edited · 1
- zedarvates/botte-secrete★ 230% same
Wrote their own version · 9
- FalkorDB/FalkorDB★ 10ksame name
- ddalcu/mlx-serve★ 2.4ksame name
- a-ghorbani/pocketpal-dev-team★ 6same name
- ai-agent-lead/skills★ 3same name
- deevus/skills★ 2same name
- smgglrs-ai/navra★ 2same name
- CorvusWing/CorvusWing★ 1same name
- leo1852098393-blip/mxops★ 0same name
- MrHashMe/efficiency-playbook★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository114 other files it gives its agents.
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- agent-compressionSkill
- agent-intelSkill
- agent-overlapSkill
- agent-stateSkill
- ambient-hudSkill
- app-testSkill
- audit-dagSkill
- auto-distillSkill
- auto-memorySkill
- auto-routerSkill
- bootstrapSkill
- botte-learnSkill
- botte-nnSkill
- botte-proxySkill
- botte-wrapSkill
- cacheSkill
- call-chainsSkill
- capabilitiesSkill
- cardinalSkill
- checkupSkill
- clarificationSkill
- clusterSkill
- code-complexitySkill
- code-duplicationSkill
- code-fingerprintSkill
- code-rulesSkill
- code-stateSkill
- code-wrappersSkill
- comfyuiSkill
- completion-proofSkill
- conductorSkill
- context-budgetSkill
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- context-profilerSkill
- context-slicerSkill
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- control-loopSkill
- cost-estimatorSkill
- dag-optimizerSkill
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- demoSkill
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- The first 60 shown.
The file
Files
bench — routing benchmark methodology
Package scope and prerequisites
This catalog ships instructions, not the executable runtime. Python modules, scripts, local services and optional tools named below must be installed separately from the upstream project. Check their availability before running commands; if they are missing, explain the prerequisite instead of claiming execution or results. The host agent may use model tokens while following these instructions.
Commands, APIs and runtime behavior described below are conditional examples for a compatible upstream installation; they are not evidence of execution in this package.
python -m skills.bench.cli # table + totals
python -m skills.bench.cli --json
python -m skills.bench.cli --save md # timestamped report under .botte/reports/The upstream benchmark runs its fixed task corpus through auto_router.decide() and compares decision-based cost estimates with a declared Tier.STANDARD baseline. This is a routing simulation, not an execution of every provider call or measurement of actual bills. Record the installed corpus, cost-model version and baseline before interpreting the output.
Why this exists
A reproducible benchmark needs a versioned corpus, explicit baseline and recorded controls. Use the separately installed upstream benchmark to compare estimates across changes, and actual measured replay evidence for claims about production usage or savings. Upstream integration proposals under docs/plans/ are not included here.
Reading the result
local_pct— the share of the simulated corpus assigned to a local backend; this is not measured provider usage.totals.token_savings_pct/usd_savings_pct— belt cost vs. the fixed baseline.- Caveat, stated plainly: the corpus prompts are short (they're meant to be reproducible and fast, not exhaustive), so the effort estimator's length-based signal under-weights them relative to real production prompts — which carry full stack traces, diffs, multi-file context. On a machine with a local backend running, this bench tends to read higher local% than a real session would. Treat it as a limited diagnostic estimate, not a bound on production savings or a substitute for verified historical measurements.
Related: [[auto-router]] (what's being measured), [[control-loop]] (the real, historical counterpart), [[cost-estimator]] (the cost model reused here).
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