kc

Use Context7 CLI for up-to-date model facts (size, API format, context window) and the guidance below for "what kind of models are good for what part of a doc verification app". Consult whenever you need to pick a model for a tier slot, decide between provider alternatives, or sanity-check an existing tier assignment. Context7 gives you fresh facts; this skill gives you the heuristic that maps those facts onto KC's pipeline. Optional plugin (Context7 install: npm i -g context7).

Determine whether extracted entities comply with verification rules. Use after entity extraction to make the pass/fail judgment for each rule on each document. Covers translating natural language rules into executable logic, choosing between Python calculation and LLM semantic judgment, and producing actionable comments on failures. Also use when designing the judgment step of a workflow or when a rule's judgment logic needs debugging.

Design and calibrate confidence scoring for extraction and verification results. Use when building any workflow that needs to quantify trust in its output, when setting up quality control sampling thresholds, or when calibrating existing confidence scores against actual accuracy. Confidence is the bridge between workflows and quality control — high confidence means less review, low confidence means more review. Also use when the quality control skill reports that confidence scores do not correlate with actual correctness.

Identify, catalog, and handle corner cases that do not fit the mainstream verification workflow. Use AFTER several rounds of skill/workflow iteration have surfaced documents that genuinely don't fit and can't be accommodated by reasonable changes to the standard flow. Covers when something is a corner case vs. a systemic fix needed, how to register it, how to wire detection-and-resolve so the corner case stays out of the hot path until a similar document appears again, and when to promote a corner case to a regular rule.

Build cross-document verification rule-skills and workflows — i.e., rules where the verdict depends on facts that appear in MORE than one document. Use when authoring or distilling a rule that requires comparing entities/values across documents in a case (main contract + appendices, loan application + income certificate + bank statement, etc.). Covers what the rule definition must specify (source-doc → entity → target-doc → entity → consistency level), how the resulting check.py/workflow walks the case, contradiction types to handle, and severity classification. Distinct from `compliance-judgment` (within-doc rules); KC is the builder, not the executor.

Generate HTML dashboards for developer users to visualize verification results, system progress, and quality metrics. Use when a testing round completes, when production batches finish processing, when the developer user wants visual reporting, or when they explicitly ask for it. Dashboards are self-contained HTML files. Use this skill **when there's something visual worth showing** — not as a default deliverable. For routine status updates use KC's TUI. The dashboard is a complement to direct reporting, not a substitute.

Build intuition about document data before writing extraction logic. Use before designing any extraction schema or regex pattern, when onboarding a new document type, or when extraction accuracy is unexpectedly low and you suspect a data assumption is wrong. Covers systematic observation of raw documents, spot-checking extracted results, distribution analysis, and recognizing suspicious patterns. If you are about to write code that touches document data and you have not read at least five documents end-to-end, stop and use this skill first.

Split documents into chunks for downstream processing. Use when batching samples for observation, feeding extraction workflows, or breaking long regulation documents into pieces small enough to fit a worker LLM. Covers cheap methods (page, fixed-size, header-based) for quick exploration AND the onion-peeler hierarchical strategy + wedge fallback for production-grade chunking of long structured documents. Also covers the central balance question: chunk-too-big (information lost in a haystack) vs. chunk-too-small (semantic continuity broken).

Extract specific entities, values, and text segments from documents as required by verification rules. Use after tree processing has located the relevant section, when a rule needs a specific number, date, name, amount, clause, or any domain-specific entity extracted. Covers extraction method selection (regex vs LLM), schema design, postprocessing, and confidence annotation. Also use when designing the extraction step of a workflow for worker LLMs.

Design and execute quality control for production verification workflows. Use when workflows are deployed on Input/ documents and results need to be monitored, when designing the QC sampling strategy for a rule, or when evaluating whether monitoring can be reduced. Covers LLM-as-Judge evaluation, adaptive sampling strategies, confidence-based triage, and the transition from active monitoring to stable oversight. Also use when production quality drops and you need to diagnose whether to trigger the evolution loop.

Manage versioning of skills, workflows, prompts, and system configuration throughout the lifecycle. Use when skills are modified, workflows are regenerated, prompts are updated, or any artifact needs rollback capability. Covers what to version, how to version with file-system conventions, maintaining a version manifest, and rollback procedures. Also use when comparing performance between versions or when production results need to trace back to the exact workflow version that produced them.

用 Context7 CLI 查最新模型事实（参数规模、API 格式、上下文窗口），用下面的指南 来理解"什么类型的模型适合 doc verification 应用的哪一段"。在为某个 tier 槽位 挑模型、在多个服务商之间取舍、或回看现有分层分配是否合理时调用。Context7 给你 新鲜事实；本 skill 给你把那些事实落到 KC 流水线上的启发式经验。可选插件 （安装：npm i -g context7）。

Determine whether extracted entities comply with verification rules. Use after entity extraction to make the pass/fail judgment for each rule on each document. Covers translating natural language rules into executable logic, choosing between Python calculation and LLM semantic judgment, and producing actionable comments on failures. Also use when designing the judgment step of a workflow or when a rule's judgment logic needs debugging.

Design and calibrate confidence scoring for extraction and verification results. Use when building any workflow that needs to quantify trust in its output, when setting up quality control sampling thresholds, or when calibrating existing confidence scores against actual accuracy. Confidence is the bridge between workflows and quality control. Also use when the quality control skill reports that confidence scores do not correlate with actual correctness.

识别、登记、处理那些不符合主工作流的边缘案例。**在经过若干轮 skill/workflow 迭代之后**——确实出现了适应不了的文档、且把它塞进主流程的代价不划算——才使用本 skill。涵盖：什么算边缘案例（vs 系统性问题），如何登记，如何用"检测 + 解决"机制把边缘案例留在冷路径上、只有当类似文档再次出现时才取出来用，以及何时把边缘案例提升为常规规则。

建立"跨文档核查的 rule-skill / workflow"——即判定依赖于多份文档中事实的规则。在编写或蒸馏一条需要跨文档比对实体/数值的规则时使用（主合同 + 附件、贷款申请 + 收入证明 + 银行流水 + 征信报告等）。涵盖：规则定义里必须明确的几个锚点（源文档 → 实体 → 目标文档 → 实体 → 一致性等级）、由此生成的 check.py / workflow 怎么遍历一个 case、要处理的矛盾类型、严重性分级。和 `compliance-judgment`（单文档规则）区分——KC 是 builder，不是 executor。

生成 HTML 仪表盘，让开发者用户可视化核查结果、系统进度、质量指标。在测试轮次完成、生产批次跑完、开发者用户想看可视化报告，或他们明确要求时使用。仪表盘是自包含的 HTML 文件。**仅在确实有"值得可视化的东西"时使用** —— 不要当成默认交付物。日常状态汇报用 KC 的 TUI 即可。HTML 仪表盘是对直接汇报的补充，不是替代。

Build intuition about document data before writing extraction logic. Use before designing any extraction schema or regex pattern, when onboarding a new document type, or when extraction accuracy is unexpectedly low and you suspect a data assumption is wrong. Covers systematic observation of raw documents, spot-checking extracted results, distribution analysis, and recognizing suspicious patterns. If you are about to write code that touches document data and you have not read at least five documents end-to-end, stop and use this skill first.

把文档切成块供下游处理。在批量观察样本、给抽取工作流喂数据、或把长篇监管文档 切成 worker LLM 装得下的块时使用。涵盖：用于快速探索的便宜方法（按页 / 定长 / 按标题），以及用于结构化长文档生产级分块的"洋葱剥离"层次策略 + "楔入法"兜底 （借鉴 pdf2skills）。也涵盖最核心的平衡问题：块太大 —— 关键信息淹没在一堆草料里； 块太小 —— 语义连续性被打断。

Extract specific entities, values, and text segments from documents as required by verification rules. Use after tree processing has located the relevant section, when a rule needs a specific number, date, name, amount, clause, or any domain-specific entity extracted. Covers extraction method selection (regex vs LLM), schema design, postprocessing, and confidence annotation. Also use when designing the extraction step of a workflow for worker LLMs.

Design and execute quality control for production verification workflows. Use when workflows are deployed on Input/ documents and results need to be monitored, when designing the QC sampling strategy for a rule, or when evaluating whether monitoring can be reduced. Covers LLM-as-Judge evaluation, adaptive sampling strategies, confidence-based triage, and the transition from active monitoring to stable oversight. Also use when production quality drops and you need to diagnose whether to trigger the evolution loop.

Manage versioning of skills, workflows, prompts, and system configuration throughout the lifecycle. Use when skills are modified, workflows are regenerated, prompts are updated, or any artifact needs rollback capability. Covers what to version, how to version with file-system conventions, maintaining a version manifest, and rollback procedures. Also use when comparing performance between versions or when production results need to trace back to the exact workflow version that produced them.

Extract and organize business verification rules from regulation documents into discrete, testable units. Use when processing documents in Rules/ to identify individual verification rules, when decomposing a regulation into atomic checks, or when the developer user adds new regulation files. Covers reading regulation text, identifying rule boundaries, determining granularity, handling cross-references, and producing a rule catalog. Also use when rules are provided in structured formats like xlsx or csv.

Write each verification rule into a Claude Code skill folder following the official skill format. Use when converting extracted rules into skill folders, when iterating on existing rule skills after testing, or when the developer user wants to capture domain knowledge as a skill. Each skill folder must be self-contained with business logic in SKILL.md, code in scripts/, regulation context in references/, and sample data in assets/. Also use the bundled skill-creator for the full eval/iterate workflow.

Distill a proven verification skill into a Python workflow with worker LLM prompts. Use when a rule skill has been tested and reaches the SKILL_ACCURACY threshold defined in .env. Covers the decision of what to implement as code vs LLM calls, prompt engineering for small context windows, model tier selection and progressive downgrade, and testing workflows against the coding agent's own results as ground truth. Also use when optimizing existing workflows for cost or speed.

Extract and organize business verification rules from regulation documents into discrete, testable units. Use when processing documents in Rules/ to identify individual verification rules, when decomposing a regulation into atomic checks, or when the developer user adds new regulation files. Covers reading regulation text, identifying rule boundaries, determining granularity, handling cross-references, and producing a rule catalog. Also use when rules are provided in structured formats like xlsx or csv.

Write each verification rule into a Claude Code skill folder following the official skill format. Use when converting extracted rules into skill folders, when iterating on existing rule skills after testing, or when the developer user wants to capture domain knowledge as a skill. Each skill folder must be self-contained with business logic in SKILL.md, code in scripts/, regulation context in references/, and sample data in assets/. Also use the bundled skill-creator for the full eval/iterate workflow.

将一条已通过测试的验证 skill 蒸馏为带 worker LLM 提示词的 Python workflow。当某条规则 skill 已经过测试、达到 `.env` 中定义的 SKILL_ACCURACY 阈值时使用。覆盖如下决定：哪些部分用代码实现、哪些部分用 LLM 调用；针对小上下文窗口的提示词工程；模型层级选择与渐进式降级；以及如何用编码 agent 自己的 skill 结果作为 ground truth 来测试 workflow。也用于对已有 workflow 做成本或速度上的优化。

Decide how to decompose the rule set into TaskBoard tasks during rule_extraction → skill_authoring transition. Covers ordering methodologies (difficulty-first / Shannon–Huffman, breadth-first, depth-first, binary partition), grouping rules (when to bundle multiple rules into one task vs. keep separate), three-axis difficulty estimation, and how to write PATTERNS.md project memory that stays useful across the run. Use when entering rule_extraction, when entering skill_authoring, or whenever the TaskBoard feels wrong and you want to re-decompose.

在 rule_extraction → skill_authoring 过渡阶段决定如何把规则集拆分为 TaskBoard 任务。涵盖排序方法（难度优先 / Shannon–Huffman、广度优先、深度优先、二分切分）、分组策略（多条规则合并到一个任务 vs. 各自独立的判断标准）、三轴难度评估、以及如何写一份贯穿全流程都能用得上的 PATTERNS.md 项目记忆。当进入 rule_extraction、进入 skill_authoring 或感觉 TaskBoard 走偏想要重新拆分时使用。

Initialize and configure a document verification workspace. Use when a developer user first opens this workspace, when .env needs configuration, or when the business scenario needs to be understood. Guides the coding agent through reading regulation documents, understanding the developer user's business context, configuring model tiers and thresholds, and establishing the working relationship. Covers initial conversation with developer user to scope the verification task, set expectations, and agree on checkpoints.

Initialize and configure a document verification workspace. Use when a developer user first opens this workspace, when .env needs configuration, or when the business scenario needs to be understood. Guides the coding agent through reading regulation documents, understanding the developer user's business context, configuring model tiers and thresholds, and establishing the working relationship. Covers initial conversation with developer user to scope the verification task, set expectations, and agree on checkpoints.

Design production-grade document chunking mechanisms for verification workflows. Use when building the chunking step of a workflow that will run repeatedly on many documents. The approach: observe sample documents, find structural patterns, write a chunking script in code, that script runs in production. Also use for navigating large documents via hierarchical structure when a rule targets a specific section. For quick, cheap batch chunking during exploration, use document-chunking instead.

Design production-grade document chunking mechanisms for verification workflows. Use when building the chunking step of a workflow that will run repeatedly on many documents. The approach: observe sample documents, find structural patterns, write a chunking script in code, that script runs in production. Also use for navigating large documents via hierarchical structure when a rule targets a specific section. For quick, cheap batch chunking during exploration, use document-chunking instead.

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.

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.

Parse source documents into machine-readable text with maximum fidelity. Use when processing any document in Samples/ or Input/ for the first time, when parsed text quality is poor, or when tables and charts need special handling. Covers multi-level parser selection from simple text extraction to OCR and vision models. Also use when a verification rule fails due to parsing issues (garbled text, missing tables, mangled layouts) and the parser needs to be upgraded for that document type.

Drive continuous improvement of skills and workflows through the diagnose-classify-fix-retest cycle. Use after any testing round reveals failures, when production quality control flags issues, or when accuracy drops below thresholds. Covers failure analysis, distinguishing systemic issues from corner cases, deciding whether to rewrite or patch, and knowing when to stop iterating. The evolution loop is the heartbeat of the system. Also use when transitioning between lifecycle phases (skill testing, workflow testing, production monitoring).

Decompose each verification rule into independent sub-tasks and assign the optimal method (rule, code, LLM, manual) to each. Use when converting extracted rules into implementation plans, when a rule skill is too expensive or inaccurate and needs restructuring, or when designing a multi-step verification pipeline. Covers MECE decomposition, method selection via the four-dimension decision matrix, cost-benefit analysis, and source tagging. Also use when auditing an existing workflow for cost optimization opportunities.

Decompose each verification rule into independent sub-tasks and assign the optimal method (rule, code, LLM, manual) to each. Use when converting extracted rules into implementation plans, when a rule skill is too expensive or inaccurate and needs restructuring, or when designing a multi-step verification pipeline. Covers MECE decomposition, method selection via the four-dimension decision matrix, cost-benefit analysis, and source tagging. Also use when auditing an existing workflow for cost optimization opportunities.