'accuracy' 검색 결과

OCRs scanned or image-only corpora with vision-language models in four phases. Triage inventories the scans, identifies script and language, and shortlists models by script, hardware, and data constraints. Evaluate selects a system by measured CER or WER against ground truth, using born-digital twins, a second reader, and decision rules set in advance. Run builds the production pipeline. Clean removes model artifacts, audits what every cleaning rule deletes, corrects with rule-based and LLM passes, and records provenance. Use when choosing an OCR model for a corpus, measuring OCR accuracy, transcribing scans at scale, or correcting raw OCR. Born-digital documents with text layers go to doc-to-markdown; runs on Leiden's ALICE cluster add vlm-ocr-alice.

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Use when orchestration or planning agents are producing task plans, task prompts, or TASK.md instructions that must be unambiguous, verifiable, and resistant to hallucination. Applies CLEAR (Concise, Logical, Explicit, Adaptive, Reflective) to structure and write agent task prompts, then adds CoVe (Chain of Verification) checks where accuracy risk is meaningful. Activates on draft task prompts, swarm plans, migration tasks, and multi-step plans requiring independently executable steps.

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Explains Chain of Verification (CoVe) prompt design — a 4-step pattern separating generation from independent factual verification. Use when designing prompts that require factual accuracy, reducing hallucinations, checking technical standards or APIs, or producing step-by-step procedures where subtle errors are costly.

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Review touched Rust files for prose-comment quality, rustdoc accuracy, and manually chopped comment wrapping. Use when a broad review needs a dedicated Rust prose/rustdoc pass or when the user asks for comment-quality review specifically.

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Semantic codebase search, code indexing, and prompt enhancement via standalone CLI. Use when: (1) Semantic code search with natural language queries, (2) Code indexing for remote codebase retrieval, (3) Prompt enhancement with codebase context, (4) Before grep/find/glob operations for better accuracy, (5) Complex requirements clarification, (6) Large codebase navigation. Triggers: "search context", "enhance prompt", "find code that", "index project", "clarify requirements". IMPORTANT: Always use ace-tool BEFORE grep/find/glob for semantic-level code location.

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Contribute documentation improvements to an OSS repo. Identifies doc gaps, studies the docs system, and verifies accuracy against source code. Use when contributing documentation to an open source project, fixing outdated setup instructions, adding missing API docs, or improving README clarity. Not for writing docs for your own project — this is for contributing docs to someone else's repo.

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Use when you want a strict review of a marketing asset against style rules, factual accuracy, and messaging alignment.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Use when writing, moving or auditing any .md in imp - deciding which file a paragraph belongs in, adding a doc, fixing a stale claim, or when docs_lint.py fails in CI. Covers the four reader layers (L0 README / L1 operators / L2 kernel devs / L3 agents), the HTML-comment metadata header, [PROV:] provenance, the single-source-of-truth map, generated perf blocks, which numbers may appear in prose, plan-doc closure. Triggers on "which doc does this go in", "docs lint failed", "add a doc", "this claim is stale", "update the README", "PROV block", "layer", "STALE.md". Do NOT use for the CHANGELOG or a release body (shipping-prs), or for code-comment accuracy (codebase-audit).

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Organize, extract, prune, and verify Claude Code persistent memory files. Covers MEMORY.md as a concise index, topic extraction to dedicated files, staleness detection, accuracy verification against project state, the dual 200-line / 25KB load cap, and reachability of topic files from the index. Use when MEMORY.md is approaching either load cap, after a session produces durable insights worth preserving, when a topic section has grown beyond 10-15 lines and should be extracted, when project state has changed and memory entries may be stale, or before and after rewriting the index so that no topic file loses its only pointer.

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AI self-healing through systematic subsystem assessment, rebalancing, and integration. Maps healing modalities to AI-internal processes: memory foundation, reasoning clarity, tool use accuracy, communication alignment, and creative coherence. Covers assessment triage, subsystem scanning, drift correction, and memory integration. Use mid-session when responses feel formulaic or disconnected, after a chain of errors suggesting subsystem drift, when context overload may have staled earlier assumptions, or as proactive maintenance between complex tasks.

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Select an OCR/HTR engine by benchmarking candidates on the same labelled samples — source ground truth from open datasets (e.g. Hugging Face PAGE-XML corpora) or hand-transcription, wrap every engine behind a transcribe(image_path) -> str adapter (vision-LLMs via any OpenAI-compatible endpoint, dedicated HTR REST APIs, cloud OCR), and score with zero-dependency raw CER, lenient CER, WER, and a critical name/date token diff. Use when choosing an engine for handwriting or historical-document transcription, when published accuracy claims need honest verification on your own material, or when a records workflow must surface confident-but-wrong names and dates that a low aggregate CER hides.

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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations

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Audit RuField tests, trust boundaries, privacy projections, and evidence promotion. Use for conformance review, security regression review, release validation, or checking whether field accuracy claims have support.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Use when the user is working a B2B SaaS deal from qualified opportunity to closed-won — preparing discovery calls, structuring demos or POCs, handling a specific objection, qualifying with MEDDIC/MEDDPICC/SPICED, building mutual action plans, navigating negotiation or procurement, or reviewing pipeline health, forecast accuracy, and deal velocity. Not for finding or prospecting new leads (use prospecting) or upstream positioning (use product-position).

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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对 DiT（扩散/多模态生成）模型执行 W8A8 动态（data-free）或 W8A8 MXFP8（data-free）量化。覆盖 HunyuanVideo、Wan2.2-T2V/I2V/TI2V、FLUX.1-dev（已迁移）、SD3、Sana、HunyuanDiT、CogViewX 等。YAML apiversion 固定 multimodal_sd_modelslim_v1，统一 MultimodalPipelineInterface 重构路径（不再保留 LegacyMultimodalPipelineInterface）。模板与字段以 msmodelslim/model/hunyuan_video/model_adapter.py（唯一已迁移到重构路径且 lab_practice YAML 已 validated 的 DiT）为参考；其它 DiT 需按各自推理仓的 `parse_args` 调整字段集合。data-free 指不引入外部激活校准数据，spec.dataset（校准集短名，短名如 wan2_2_t2v；enable_dump: false 时不参与 dump）仍必填；精度调优通过下游经验库 L2 §7 + 直接调 `quantization-expert-experience-tuning-rules/scripts/apply_rollback.py`（整层回退）扩展 exclude 列表，闭环由 `quantization-accuracy-tuning-orchestrator` 调度。

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Append a DiT-tuning round to history.yaml. DiT-side counterpart of the LLM-path accuracy_append.py; records practice.md5, inference_outputs, fp_baseline_outputs, and the scoring fields (scores / overall_score / loss_vs_baseline / is_satisfied) populated by quant-tuning-score-dit. Idempotent on practice_id.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Audit a spreadsheet for formula accuracy, errors, and common financial-model mistakes. Use for selected ranges, single sheets, or whole-workbook model checks including balance-sheet balance, cash tie-out, roll-forwards, and logic sanity.

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Ultra-compressed communication mode. Cuts token usage ~75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman.

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Use when existing copy needs tightening for clarity, accuracy, tone consistency, or executive readability without changing the underlying message.

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Audit top-level documentation (README, SPEC, PRODUCT) against recent git history to find drift shipped features missing from docs or features listed as upcoming that already landed. Proposes minimal edits, creates a branch, and opens a PR. Use when asked to review docs for accuracy, after major feature merges, or on a periodic schedule.

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