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).