Lx050

rubbing-to-knowledge

面向碑帖、拓片、古籍与铭刻研究的证据优先编排 Skill。用于把可证伪问题路由到材料采集、溯源、图像处理、OCR、版本定位、文本结构、反思、审计与知识网络组件，或组合外部 Skill/SCP；所有外部与机器输出先作为候选，只有固定载体、明确定位和受信复核才能进入证据层。也用于运行六工具 edition-evidence SCP，生成可审计 dossier，或在权利、身份、证据不足时返回类型化阻断。

epigraphy-knowledge-network

Turn located, human-adjudicated epigraphic text and located literature evidence into an auditable knowledge graph of candidate mentions, human-adjudicated assertions and evidence-backed edges, with deterministic timeline, relationship-path and entity queries. Use when Codex must build, verify, query or export a rubbing-derived knowledge graph without letting OCR, AI proposals, name matches, modern place names, calendar conversions or graph paths become historical facts.

epigraphy-text-structure

Validate qualified epigraphic transcriptions; draft append-only, evidence-linked proposals for punctuation, annotation, character normalization, metadata, people, places, offices, era dates, relations, and ambiguities. Use when structuring a human-reviewed rubbing transcription, a located source-edition transcription, or an explicitly synthetic test fixture without treating OCR, AI suggestions, normalized characters, modern places, calendar conversions, or entity identities as fact.

research-hypothesis-reflection

Review a ResearchCase v1 after evidence or experiment feedback arrives. Use when a research workflow must detect supporting-versus-challenging evidence conflicts, negative/null/invalid results, dependent evidence, or stalled inquiry; preserve prior hypotheses in a versioned ReflectionMemo; and choose only continue, reroute, rollback, human_review, or stop_unresolved.

AI & Automation Listed

research-integrity-audit

Audit the ResearchCase v1 publication gate for missing evidence, HYP or rejected support, unresolved conflicts, unadjudicated machine output, unaccountable adjudication records, broken hash bindings, sticky blockers, evidence-layer confusion, and vacuous completion attempts. Use before packaging or publishing claims, after claim-evidence links change, or when a workflow needs a machine-readable pass/warn/block decision.

research-literature-evidence

Build an append-only, auditable path from preregistered literature searches to precisely located evidence candidates while keeping snippets, OCR, AI interpretations, self-attested human review, synthetic fixtures, and unclear rights from becoming academic or public evidence. Use for books, papers, catalogues, webpages, inscriptions, and other research sources when queries, discoveries, failures, editions, carriers, locators, excerpts, conflicts, and review gates must remain reproducible.

Data & Documents Listed

research-provenance-ledger

Register local research materials in an append-only provenance ledger, capture SHA-256, size, MIME, source, license, and rights status, and reverify originals or copies without executing them. Use when a research workflow needs auditable material intake, duplicate-ID protection, integrity checks, or explicit hash-mismatch reports before OCR, analysis, publication, or packaging.

Data & Documents Listed

research-quarantine-promotion

Safely assess one already verified quarantine JSON as bounded inert data, using a preregistered exact binding, an exclusive source-verification receipt, same-buffer identity and strict parsing, content-free structure reports, and offline producer verification. Use when research-source-acquisition has preserved an application/json asset in quarantine and a later internal workflow needs an auditable B0 gate before any IIIF, OCR, schema, literature, or knowledge-graph interpretation; do not use for arbitrary JSON, URL following, semantic validation, canonical materialization, academic admission, or public release.

AI & Automation Listed

research-run-ledger

Execute approved research commands without a shell and preserve tamper-evident command events, exit status, separate stdout/stderr artifacts, exact input/output snapshots, and verified append-only output checkpoint chains. Use when a research workflow, experiment, data conversion, growing JSONL ledger, model invocation, validation, or multi-Skill handoff must be reproducible and auditable at command level.

rubbing-image-preparation

Inspect and deterministically preprocess digitized rubbings, steles, copybooks, and inscription images before OCR or scholarly comparison. Use when Codex must measure image quality, reject unsafe or unsupported image inputs, preserve an immutable parent hash, create grayscale/contrast/polarity baselines, compare preprocessing variants, or register non-generative derived assets without presenting enhancement as recovered historical evidence.

rubbing-ocr-review

Run auditable OCR baselines on registered rubbing, stele, copybook, and inscription images, preserving empty or failed results, and create append-only, actor-declared review decisions and pending human-review handoffs. Use when comparing OCR across an original and deterministic derivatives, binding readings to image hashes and pixel regions, retaining uncertain or rejected readings, separating AI prelabels from declared-human review, or handing OCR evidence to text-structure workflows.

rubbing-research-orchestrator

Orchestrate provenance-first research on rubbings, steles, inscriptions, and related historical sources. Use when Codex must initialize a ResearchCase, split an epigraphy question into auditable stages, route work across independent Skills or Agents, react to positive/negative/null/invalid results, enforce research gates, or decide whether to continue, reroute, request human review, stop unresolved, or package a study.