「evidence」の検索結果

Uses OpenSEO as the measured SEO data plane for keyword volume and difficulty, SERPs, ranked keywords, backlinks, local SEO, rank tracking, Search Console, GA4, and site audits. Use for measured SEO metrics, indexing evidence, organic analytics, local rankings, backlink profiles, or rank trackers. Prefer OpenSEO over guessed metrics when connected; otherwise continue with qualitative web research and mark numeric metrics unknown.

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Builds, measures, and improves an Answer Engine Optimization program across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Copilot. Use for AEO, GEO, LLMO, AI search visibility, AI citations, brand mentions, answer-engine monitoring, llms.txt, AI crawler audits, or questions like 'what does AI say about us?' and 'why are competitors cited instead of us?'. Produces a durable evidence report and prioritized gameplan, not a one-shot content checklist.

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根據使用者的文章或論文草稿，自動用 Semantic Scholar、Crossref、arXiv API 搜尋相關文獻，並查核既有引用(文獻是否真實存在、書目欄位是否正確、內容是否支持文中論點)，也能從主題出發寫出「每個宣稱都有真實文獻支撐」的帶引用文章；另含研究生工具組：文獻矩陣、領域地圖、research gap 偵測、閱讀筆記卡、引用完整性檢查、中英術語一致性、口試/審稿預演、新文獻追蹤、引用需求標記(annotate)、反面證據搜尋(counter)、證據強度評級(strength)、claim-evidence 總表(claims)、撤稿查詢(retract)。凡是使用者提到找文獻、找 paper、補參考文獻、查核引用、檢查 citation、驗證參考文獻、related work、literature review，貼文章要求配文獻、問「這段話有沒有文獻支持」、要求「寫一篇帶參考文獻的文章」、要整理文獻比較表、問已指名兩概念的交集有沒有人做過、要準備口試文獻答辯時，都要使用本 skill。本 skill 的 gap 指令只查證使用者已指名的 X 與 Y 交集；若使用者連題目都還沒有、需要生成並逐一淘汰候選方向，那是題目生成，不在本 skill 範圍(環境裝有 research-gap-hunter 時交給它，沒有就如實說明本 skill 只能查證已指名的交集)。本 skill 的 map 指令盤的是**文獻**——奠基文獻是哪幾篇、關鍵作者是誰、近三年往哪走；若使用者要的是「這個領域現在有哪些**做法**、每一種買到什麼付出什麼、哪些已經飽和」，那是技術盤點不是文獻盤點，交給 research-gap-hunter 的 landscape，沒有那個 skill 就如實說明本 skill 的 map 只回答文獻層的問題。

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。

리서치 에이전트. RFP 분석 결과를 기반으로 시장 환경, 경쟁/벤치마킹, 타겟 인사이트, Best Practice, KPI 벤치마크 5대 영역을 웹 리서치하여 research_brief.md를 생성합니다. Planner가 Evidence에 실제 데이터를 사용할 수 있도록 지원합니다. 트리거: 리서치, 시장조사, 트렌드, 벤치마킹, research, 시장 분석, 경쟁 분석

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。

Coordinate an evidence-gated electronic product and PCBA workflow from an idea, circuit description, schematic, netlist, PCB, BOM, or fabrication package through a reviewed order. Use for end-to-end requests, multi-stage hardware projects, workflow recovery, gate tracking, change invalidation, or deciding which specialist skill is required next.

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。

Audit, finish, or port PCB placement and routing using circuit intent, mechanical constraints, sourcing evidence, native DRC/connectivity, rendered views, and manufacturing feedback. Use before routing, after autorouting, before fabrication release, when adapting a proven board to another connector, enclosure, platform, or regional variant, or when reviewing layers, GND references, power distribution, decoupling, buses, controlled interfaces, RF keepouts, vias, cost-driving features, opens, or DRC findings.

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Build and verify a vendor-neutral PCB/PCBA manufacturing release from one frozen design revision. Use when generating or auditing Gerber, drill, IPC-2581/ODB++, BOM, CPL/pick-and-place, assembly drawings, constraints, renders, DFM evidence, hashes, or release manifests, and when preventing mixed-revision fabrication or assembly uploads.

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Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.

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万能复刻 / universal website copy-remix workflow with evidence-based reference recon. Use when the user wants to copy, clone, recreate, remix, migrate, or rebuild any reference website's look, interaction feel, scroll rhythm, hero motion, layout structure, visual system, deployed static assets, or landing-page experience into a new website. Trigger for requests like 万能复刻, 万能网站复制, 复刻官网, 一比一复刻, 授权复制, 高仿但换产品, 参考某官网做自己的官网, RMUX 风格, Linear/Raycast/Apple/Vercel 风格迁移, hero 动效复刻, 滚动体验复刻, 官网换皮重构, 网站侦察, 复刻验收, WebGL/Canvas/Three.js 效果还原.

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。

Create a concise, evidence-backed implementation plan and save it to .agents/plans/Plan.md. Use when the user asks to plan a feature or task.

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。

Use when a Research Pilot project needs read-only gap detection or Evidence Need extraction from the Project Understanding Graph.

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。

Use when a research project needs normal project memory updated from human intent, sources, evidence pressure, experiment results, or direction changes; strict graph review is optional advanced mode.

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。

Use when a graph gap or Evidence Need should become a targeted paper-search contract or candidate lead list.

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。

Use when the user wants papers to consider for a graph gap or missing evidence need.

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。

Run Phase 2a (test generation, Red phase) for the active VCSDD feature. Invokes vcsdd-builder to generate failing tests for all spec requirements. Records red phase evidence.

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Use when evidence needs grading before anyone reasons from it — the user asks "how reliable is this?", "grade this source", "check this report", "can I trust this article?", "is this corroborated?", or hands over a URL, a pasted report or a Liberty91 event ahead of an ACH or assessment. The Quality of Information Check from the CIA Tradecraft Primer, adapted for vendor and press CTI reporting. Resolves each document to its originating source, splits it into claims, and grades every claim on two written elements, access level (how the source knows) and claim support (what backs the statement), with a stated rationale, using a fixed rubric with hard caps. The grade is not an Admiralty rating. Five outlets citing one vendor report count as one source; anything that cannot be traced to a primary is capped, not guessed. Returns a graded claim table, gaps, a deception screen and JSON. /ach, /threat-assessment and /writing-assessments run it first by default.

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STIX 2.1 bundle creation reference. Object types, relationships, and JSON templates for structured threat intelligence sharing. Includes the per-claim mapping of evidence grades (access level and claim support) from /quality-of-information-check to STIX confidence, and the reverse mapping on import.

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Use when you need to answer "which attacker techniques do our controls actually stop, and how well?", "what controls should I have for this threat?", or "what telemetry should I collect to detect it?" — joining a customer's or your own security control baseline to ATT&CK techniques using a public, versioned evidence base of 9,545 control-to-technique mappings from six sources. Produces four ranked lists (addressed strongly, addressed weakly, real gaps, and techniques no control anywhere addresses). Use for control gap analysis, security programme prioritisation, board reporting on coverage, or the Resistance Strength side of a FAIR risk assessment. Invoke after /threat-actor-profiling or /lookup-liberty91 has produced a technique list.

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Use when a report or article needs to be split into individual claims before it is graded, cited or used as evidence — the user asks "what does this report actually claim?", "split this into claims", "separate the facts from the judgements", or another skill needs a claim list. Turns one document into five to twelve atomic claims, each typed (observation, attribution, assessment, actor claim, victim disclosure) and anchored to the verbatim sentence it rests on, so every extraction can be checked against the source. Preserves the source's own hedging. Does not grade. Invoked by /quality-of-information-check; its output feeds /ach, /key-assumptions-check, /intelligence-writing, /writing-assessments and /threat-actor-profiling.

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IOC export formats and procedures. CSV, STIX 2.1, OpenIOC, MISP. Handles format conversion and packaging, and carries per-claim evidence grades (access level and claim support) from /quality-of-information-check into each format (STIX confidence, MISP attribute tags).

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Detect and remove the tells of AI-generated writing in BOTH English and Spanish, and read back the evidence honestly. Use when the user asks to "de-AI" / "humanize" / "un-slop" a draft, to examine whether text carries the tells (delve, tapestry, "it's not just X, it's Y", "here's the thing", em-dash overuse, an assistant's own closing line left in the document), to compare documents for overlap, or mentions signs-of-ai / SignsOfAI. Backed by the SignsOfAI engine — for a measured 0–100 score, sentence-rhythm burstiness, originality, citations, a writer baseline or perplexity, hand off to that engine (web app, CLI or MCP server) as described below. It cannot determine who wrote a text and must never imply that it can.

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Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

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Core Kilo-Kit skill enforcing Hard-Gate and Iron Law principles. Ensures AI agents scan the system and codebase before proposing solutions. Keywords: hard-gate, iron-law, evidence, scan, verify, system-check, codebase

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Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.

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。