“baseline”的搜索结果

Audit whether a paper's baseline comparisons are COMPLETE, FAIR, and SIGNIFICANT: a required recent SOTA baseline is missing while 'best/SOTA' is claimed (HP-MISSING-BASELINE); a baseline is undertuned / given less compute-tuning-data, run at a mismatched config, or the equal-budget ablation-as-baseline is absent (HP-WEAK-BASELINE); 'outperforms' is asserted over overlapping error bars or with no variance/seeds (HP-SIG-OVERLAP); and a cross-row 'improves over baseline by X%' is arithmetically wrong (HP-DELTA-ERROR, cross-row form only). A versioned per-domain baseline profile + a live leaderboard/recency search are assembled by the EXECUTOR as structured facts; a fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per dimension) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Works at L0 (stated comparisons) and deepens at L2 (configs/result files). A completeness question it cannot settle internally becomes needs_external_check, never a guessed missing baseline. Emits baseline-comparison-audit.findings.json; computes NO verdict. Detect-only. Triggers: "baseline audit", "missing baselines", "is the comparison fair", "weak baseline", "baseline 误报", "SOTA earned?".

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Content-addressed baseline-archive epoch partitioning. Partition baseline_archive.jsonl + the self-improving hub's baseline serving by a HASH of the full baseline-production+measurement spec — when the spec (margin_rule, fitness/margin logic version, the 4 roles' model+source, rubric/dim-set, bench, seed-pool identity) changes, the hash changes → a new epoch series (like seed-gen gen-*). Triggered by "baseline epoch", "baseline 아카이빙", "epoch partition", "spec hash", "content-addressed", "margin_rule namespace", "production logic 구분", "baseline 하위 서빙" keywords.

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Compare baseline and current Pinot benchmark results from JMH, a tool for measuring code performance. The skill uses a separate baseline worktree, a working directory kept apart from the current code.

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• Compare Pinot benchmarks before and after a code change
• Check performance differences against a baseline
• Keep baseline code in a separate working directory

Mint a new YYYY-MM-DD-exact-coding-baseline snapshot under research/workflow-dev/export/. Detects the current best correctness- oriented workflow from research/workflow-dev/workflow-construction.md (or takes an explicit source-workflow argument) and transforms it from a lab workflow into a consumer-ready one on three axes: strips lab-only measurement content, re-enables human-in-the-loop checkpoints, and converts auto-loading config into an explicitly-invoked skill. Supports both promoted EXACT Coding lines: Opus/Hybrid and SOL/Predictive TDD. Exports Claude Code, pi, OpenCode, cursor-agent and, for SOL, Copilot. Trigger when the user says "exact-coding baseline export", "neue exact-coding baseline", "exact-coding-baseline-export", or asks to refresh the baseline snapshot.

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Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

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• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Establish baseline → snapshot → compare → history monitoring for any KPI, config, or metric that can drift during PRD v0.8 Deployment & Ops. Triggers on requests to monitor drift, baseline a value for later comparison, or when user asks "how do we track if X changes?", "baseline this", "config drift", "performance regression", "metric drift", "compare to last week", "is this getting worse?". Outputs MON-DRIFT-* entries with baseline + comparison rules.

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Agent fallback for CDelayedCall2_CServerSideClientBase_ProcessBaselineAckCall_vtable (auto-generated, category: func). Locate CDelayedCall2_CServerSideClientBase_ProcessBaselineAckCall_vtable in the CS2 engine module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CDelayedCall2_CServerSideClientBase_ProcessBaselineAckCall_vtable, CDelayedCall2_CServerSideClientBase_ProcessBaselineAckCall_vtable

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Monitor daily AWS spending by service and account, compare it with historical baselines, and send Slack alerts with cost differences and likely causes. Known planned increases are excluded, and cloud resources remain unchanged.

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• Detecting unusual increases in daily AWS spending
• Investigating cost spikes by service and account
• Excluding planned or seasonal spending increases

Save a page's SEO settings and compare them with later versions to detect changes after updates or deployments. Review differences in titles, headings, indexing controls, structured data, and available performance metrics, with severity levels and stored comparison history.

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• Compare SEO settings before and after deployment
• Check for unintended title or heading changes
• Investigate page changes after a traffic drop

Refresh the current snapshot of official Anthropic documentation while keeping the baseline unchanged. Use the returned preview to check differences against that baseline, then access a separate command for the full detailed comparison.

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• Refreshing a saved copy of official documentation
• Updating the current version while keeping a baseline
• Previewing changes before a detailed comparison

Fetch official documentation from Anthropic and save local snapshots through an MCP tool. Initialize both baseline and current copies, then report the section count and storage path so later refreshes can be compared against the baseline.

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• Saving an initial local copy of official documentation
• Establishing a baseline for later document comparisons
• Checking the section count and snapshot location

Captures, masks, compares, and updates screenshot baselines for web and mobile suites, with per-region thresholds and a reviewed-diff rule for every baseline change. Use when a screenshot assertion fails, a baseline needs updating, or visual checks are being added; not for deciding what to verify.

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Establish and maintain longitudinal baselines of CLI binary contents across versions. Covers marker selection by category (API / identity / config / telemetry / flag / function), weighted scoring, threshold-based system-presence detection, and per-version baseline records. Use when tracking a feature's lifecycle across releases, when probing for dark-launched or removed capabilities, or when verifying that a scanning tool itself still catches known-good markers on old binaries.

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Establishes behavioral baselines from historical telemetry (process, network, or logon events) so hunts can flag rare and first-seen activity instead of relying on static signatures. Activates for requests to build a telemetry baseline, find rare or first-seen activity, or compute frequency baselines for anomaly hunting.

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Dev-only check that a drafted spec for THIS baseline repo won't ship dev-tree references to consumer installs. Catches three failure modes — shipped SKILL.md prose that references paths under `src/`, `tests/`, `scripts/`, `obj/` as runtime invocations (in ```bash fences``` OR `inline backticks`, plus shipped `.mjs`/`.js`/`.sh`/`.py` helper-file imports); new Python helpers added under `.claude/skills/<slug>/` (shipped helpers must be `.sh` or `.mjs`/`.js` going forward); and imports of modules that aren't in `obj/template/.claude/manifest.json` (consumer won't have the file). The aggregate scanner (`scan-shipped-skills.mjs`) walks only baseline-owned skill dirs (via `owner: baseline` frontmatter) at top level — `references/` and other subdirs are documentation, not runtime. BLOCKER findings hard-block implementation entry; ADVISORY surfaces but doesn't block. Read-only — surfaces a punch list; maintainer edits the spec and re-runs until CLEAN.

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新しい Web API / CSS の利用追加に対し、Baseline 状態とブラウザ互換性 / progressive enhancement の有無を suggestion で示す。

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测量网页加载与交互、API响应以及构建和测试的耗时，对比修改前后的结果，并以通过Git共享的基准值检查性能是否下降。

• 比较代码修改前后的性能
• 调查页面缓慢的反馈
• 发布前检查性能目标

Measure web page loading times, rendering milestones, and resource sizes in a real browser. Save baseline measurements, compare results before and after code changes, and inspect historical trends to spot performance regressions.

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• Saving performance baselines before changes
• Checking page speed after a code change
• Finding slow resources and large transfers

Compare actual AI session costs with estimates for using only Haiku, Sonnet, or Opus. The skill reports savings amounts and percentages, helping teams assess model selection costs while leaving differences in answer quality and retries outside the calculation.

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• Report model selection savings during cost reviews
• Check savings after changing model selection settings
• Use JSON results to enforce a savings threshold in CI

Evaluate 3D point clouds and movement trajectories against reference data with CloudAnalyzer. Prepare inputs, measure differences, apply quality thresholds, inspect results visually, and track baseline history for mapping, localization, and perception workflows.

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• Compare a generated point cloud with reference data
• Measure errors in estimated movement trajectories
• Check ground segmentation against quality thresholds

Process calibrated one-dimensional NMR signals into spectra, candidate peaks, and signed integration results. Supports manual phase adjustment, baseline correction, and recorded processing settings so results can be reviewed and reproduced.

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• Convert raw one-dimensional NMR signals into spectra
• Check peak positions against reference signals
• Review phase and baseline corrections

Analyze authentication logs to spot suspicious sign-ins, password attacks, and possible account compromise. Statistical baselines and machine learning help compare login activity with normal user behavior, while example SIEM queries support security investigations.

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• Investigating suspicious sign-in alerts
• Detecting geographically inconsistent logins
• Finding brute force and password spraying

Set up an organization-wide Google Cloud foundation with 17 baseline security policies, environment folders and projects, billing links, and centralized audit logging and monitoring. Check the resulting setup against a defined validation checklist.

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• Set up the initial foundation for a Google Cloud organization
• Organize production, non-production, and development resources
• Apply baseline security policies across an organization

Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.

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• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies