本文へ移動
cccskills
無料GitHub で公開

content-verification-layer

Phase 4 Turn 2 of disease-market-sizing-orchestration (A6' upgrade). Audits compose output via Cite-or-Block strict — every anchor must reverse-resolve to an actual file in sources/, every fact claim must have an anchor. Returns verdict (ok/blocked) + violations list. Use AFTER cite-bound-content-generator (Turn 1), BEFORE report-bundle-builder (phase 5).

インストール方法を見る

含まれるファイル(3)

  • SKILL.md5.8 KB
  • scripts/_token_classifier.py1.9 KB
  • scripts/verifier.py55.3 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Content Verification Layer (Phase 4 Turn 2)

Strict Audits

  1. enforce_citations_in_text(html) — flag fact-bearing claims without anchors
  2. anchor existence reverse-lookup — every anchor source_id must have a backing file in sources/
  3. numeric support gate — every market/statistical number in a claim must appear in the cited source/model evidence after normalization; derived numbers must be saved in and cited from a model/evidence file
  4. report substance gate — block source inventory/process filler, deferred worker/model language, and toy Mermaid diagrams even when anchors resolve
  5. drug-bearing sections route to A5' citation verifier; unsupported drug mentions are critical
  6. strict locator gate — production anchors must include locator context; a numeric claim anchored only as [guideline:ID] or [evidence:path] is not strict evidence.
  7. claim-level audit output — verify_full_report() must return enough detail for downstream gates to audit total claims, total anchors, strict locator coverage, verified count, anchors without locator, and violation reasons. A tiny {"verdict":"ok"} file is not an acceptable production audit. When semantic Tier 2 is enabled, verification_report.json must also record semantic_audit.audit_log_path, audit_log_entries, calls_remaining, and call_budget; the referenced JSONL log must exist and line counts must match.
  8. Mermaid numeric-source audit — when rendered Mermaid sources are present, numeric labels must be checked against their nearest/declared anchors with the same numeric support rule used for visible body claims.
  9. source-channel utilization audit — when production recall fetched required channels such as ClinicalTrials.gov or PubTator/entity evidence, verify_full_report(..., required_source_channels=...) must block if the channel has saved source files but no matching body anchors.
  10. formula/model dominance audit — saved formula/model evidence can support derived numbers, but it cannot dominate body citations. If formula anchors are the main citation class, the output is a model dump, not a V25-level report.

Report Substance Gate

Verification must reject HTML that is only report-shaped. These are critical blockers:

  • visible body dominated by "已保存于 sources" rows, source-index tables, or repeated 附录证据说明
  • final report language that says work remains for a worker or later quant model
  • shallow Mermaid blocks such as A-->B diagrams without real pathway logic
  • Mermaid blocks must be audited even when the <div class="mermaid"> tag carries extra attributes such as data-chart; raw HTML attributes cannot bypass shallow-diagram checks
  • repeated 证据消化矩阵 blocks in the report body instead of narrative synthesis
  • formula/model evidence anchors accounting for a dominant share of body citations
  • numbered template padding such as 场景1/场景2/... or Block 1/Block 2/... with the same sentence body; normalize the numbering before repetition checks

Passing Cite-or-Block syntax is necessary but not sufficient for a production smoke pass.

Source Channel Utilization Gate

Production runs may pass required_source_channels=("clinical_trials", "pubtator") or equivalent. If files exist under the corresponding current-run source folders, the final report must use matching anchors in body analysis:

  • ClinicalTrials.gov / AACT → [nct:...] or [aact:...]
  • PubTator/entity recall → [pubtator:...]

Appending a source list is not enough. If a channel was fetched but excluded from analysis, the exclusion must happen before verification with a recorded reason, not by silently ignoring the saved sources.

Numeric Evidence Gate

Do not accept "数字 + existing anchor" as proof. For every numeric fact such as percentage, patient count, currency, incidence, time window, or market size, verify:

  • the cited source text contains the same number after comma/whitespace normalization, or
  • the claim cites a saved model/evidence file that contains the formula, input anchors, units, and derived result.
  • the anchor includes locator context that points at the relevant abstract, section, line, or evidence-file line.

If the number is absent from the cited source/model evidence, return a critical violation with numeric token miss.

For long captured sources, the semantic fallback excerpt must be built around the matched locator, numeric tokens, or nearby claim terms. Do not pass only the first page/header of a guideline or HTML capture to semantic verification; website navigation text is not the evidence window.

Audit Output Contract

verification_report.json must keep the audit trail needed by the production smoke gate:

  • verified_count
  • anchor_audit.total_claims
  • anchor_audit.total_anchors
  • anchor_audit.verified_count
  • anchor_audit.anchors_without_locator
  • anchor_audit.violations_by_reason
  • semantic_audit.audit_log_path / audit_log_entries / calls_remaining / call_budget when semantic verification writes a JSONL log

Schema lint must fail if these fields are written but not declared in schemas/verification_report.schema.json.

Functions

  • verify_section(section_html, sources_dir) -> Verdict
  • verify_full_report(html, sources_dir) -> Verdict
  • enforce_citations_in_text(html) -> list[Violation]

P0 守门

Tests grep skill source for hardcoded drug names / disease names — CI blocks on hit.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**:本 skill 走批量 SQL · 离线大数据(PostgreSQL);v2 走实时 API · 单查询。两者互补:单条 NCT 实时状态用 v2,百万级历史挖掘用本 skill。支持云端公共 PostgreSQL(aact-db.ctti-clinicaltrials.org · 零部署)和每日 dump 本地还原(高性能 · 离线)两种连接方式,自动检测优先用本地。跨平台(macOS/Linux/Windows)参数化 SQL 防注入,read-only 强制保护。

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据库进行大批量临床试验历史分析与数据挖掘。触发场景包括:AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、clinical trials data warehouse、疾病领域全景分析、设计相似试验匹配、跨年度试验趋势聚合。本 skill 通过 SQL 接口处理百万级试验记录,支持云端公共 PostgreSQL 服务(aact-db.ctti-clinicaltrials.org)和每日 dump 本地还原两种连接方式,自动检测并优先使用本地高性能模式。

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

Use when work produces a deliverable, factual claim, dataset, code change, decision, publication, deployment, automation, or irreversible action whose failure would matter; when the user asks for a quality gate, acceptance criteria, QA, completeness, validation, audit, evidence, preflight, release readiness, or a definition of done; or before claiming completion on medium- or high-risk work. Automatically decide whether a formal gate is warranted, derive task-specific pass/fail criteria, gather evidence, and block unsupported completion. Make sure to use this skill even when the user does not say "quality gate" if consequential work needs acceptance criteria or completion evidence. Skip formal gating for trivial, reversible, low-impact requests.

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

Create a changeset file in .changeset/ that describes a user-facing change for the release notes

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

Add a new competitor to the AI Visibility Tool Directory — researches the tool, generates data, takes a screenshot, and inserts into the codebase

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

add-lang

無料

Add tree-sitter language support to codegraph end-to-end — wire the grammar + extractor, write tests, then benchmark extraction quality and retrieval value on 3 popular real-world repos. Use when the user runs /add-lang <language> or asks to add/support a new language (e.g. Lua, Elixir, Zig, OCaml) in codegraph.

日本語の概要は準備中です。原文の説明を表示しています。

EthanYoQ/Skill-hub112026年10月5日 更新

EthanYoQ のスキルをすべて見る

このスキルの問題を報告する