Compare saved benchmark runs over time to track win rate, response times, escalation rate, and LLM baseline costs. Review changes between the first and latest runs, with flags for declining win rates or rising average latency.
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Read every docs/benchmarks/runs/*.json and surface drift in win rate, latency, escalation rate, and LLM-baseline cost over time
安装前可以查看交给 AI 代理的指令内容。
The smoke gate is binary (winRate ≥ 0.80 → pass/fail). The corpus benchmarks captured over time form a curve — and curves catch regressions the gate misses (win rate slowly creeping from 100% to 85% is "still passing" by smoke but a real degradation).
This skill reads every persisted run in docs/benchmarks/runs/*.json and reports first→last deltas plus a per-run series, flagging regressions in win rate or latency.
agent-booster — surface latency / strategy changes.Run the trend script from the project root:
node plugins/ruflo-cost-tracker/scripts/trend.mjs
Optional env:
TREND_FORMAT=json — emit JSON instead of markdownTREND_LIMIT=10 — consider only the most recent N runsInspect the drift summary — first vs last on win rate, avg latency, p99, escalation rate, speedup vs Gemini.
Inspect the per-run series — one row per run, including Sonnet 4.6 + Opus 4.7 baseline latencies if those were enabled (BENCH_ANTHROPIC=1 at run time).
Regression flags — the script emits > ⚠ Regression callouts when:
cost-benchmark — the producer of the run JSONs this skill consumesbench/booster-corpus.json — the corpus version is recorded in each run, so trends across corpus versions remain interpretabledocs/benchmarks/runs/latest.json — the most-recent run; smoke step 23 gates on winRate ≥ 0.80 from this file还没有评价。欢迎分享你的使用感受。
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
中文简介正在准备中。当前显示英语简介。
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
中文简介正在准备中。当前显示英语简介。
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
中文简介正在准备中。当前显示英语简介。
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
中文简介正在准备中。当前显示英语简介。
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
中文简介正在准备中。当前显示英语简介。
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.
中文简介正在准备中。当前显示英语简介。