'poc' 검색 결과

Sets up an `## Agent skills` block in AGENTS.md/CLAUDE.md and `docs/agents/` so the engineering skills know this repo's issue tracker (GitHub or local markdown), triage label vocabulary, and domain doc layout. Run before first use of `to-issues`, `to-prd`, `triage`, `diagnose`, `tdd`, `improve-codebase-architecture`, or `zoom-out` — or if those skills appear to be missing context about the issue tracker, triage labels, or domain docs.

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Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

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Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

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System reference for the crouton layout engine (@fyit/crouton-layout) — the LayoutTree/layout_configs data model, the croutonLayoutBlocks registry and sizing contract, the viability metric, the deterministic placer (#709), and how layouts persist and seed. Use when reasoning about WHY a layout renders/composes/persists the way it does, when a question is shaped like "what is a LayoutTree / where do layouts live / why did the placer pick this arrangement / why won't this pane shrink / where does minWidth get read", or when a generated POC boots with the wrong (or no) default layout. Trigger phrases: "layout tree", "layout_configs", "viability check", "composeDefaultLayout", "block sizing contract", "why is my layout not viable". For authoring a block component, use the block-authoring skill instead.

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Propose the test(s) FIRST and get a human to sign off on the behaviour before writing the code — the test analog of /schema-review and /ui-proposal. For hand-written LOGIC in packages/* (apps opt-in, pocs exempt). Renders the proposed failing test as a reviewable contract, holds for lgtm/approve, then you write the code to make it green. Use when an agent is about to implement packages/* logic, or when asked to "write the test first", "TDD this", "agree on the test", run /test-review.

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Volume-based PUMP token reward system with day-indexed epochs, pro-rata distribution, accumulator tracking, and cross-program claiming on Solana.

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Generate a runnable exploit-validation command (Nuclei template, Metasploit module hint, AI-assisted Python script, or curl-based PoC) against a user-specified authorised target. Use when validating that a fix actually closed the vulnerability path, confirming a patch deployed correctly, or producing a copy-pasteable test command for QA. The skill never executes — the user runs.

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April 29, 2026 Key Takeaways PocketOS, a company that designs software for car rental businesses, ha

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Prepare and launch LoRA, LoKr, or T-LoRA training through lora-scripts-next (SD-Trainer), Anima-first. Use when the user asks to train a character, style, or concept LoRA, choose parameters or repeats and epochs, start a run, or monitor training on Anima, SD1.5, SDXL, or Flux. Organize and caption the dataset, run dataset-doctor, choose a documented starting preset from image count and VRAM, show one confirmation card, then call the mikazuki API and monitor its log.

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Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire, executing sales sequences, or coaching qualification. Also triggers on: outbound, cold outreach, deal strategy, pipeline, battle card, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss.

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在授权渗透测试中挖掘跨站请求伪造（CSRF）及其防护绕过。当目标存在状态变更操作（改邮箱/密码/角色/设置、转账、增删）而 CSRF 防护缺失或可绕过时使用——典型场景：无 token/token 不校验、SameSite=Lax 被 GET 或子域绕过、Content-Type text/plain 避开 preflight、cookie 注入伪造 token、Flash/307 重定向绕 CORS、Login CSRF。适用目标类型 Web / REST API。触发场景包括用户说"测下 CSRF""这个改密码有没有 CSRF 防护""SameSite 能绕吗""token 校验严不严"。输出：CSRF 可行性 + PoC 的 finding（含 killed 记录）。

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在授权渗透测试中挖掘开放重定向（open redirect）及其危害升级。当目标按用户可控参数做跳转（登录后 next、SSO returnUrl、注销、短链、回调）时使用——典型场景：绕过 URL 白名单跳到外站钓鱼、泄露 token/Referer、开放重定向作为 SSRF/OAuth token 窃取的跳板。适用目标类型 Web / REST API / SSO。触发场景包括用户说"测下开放重定向""这个 next/redirect 参数能跳外站吗""绕过重定向白名单""open redirect 偷 token"。输出：可跳外站的参数 + PoC + 危害升级的 finding（含 killed 记录）。

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在授权渗透测试中挖掘跨站脚本（XSS：反射型、存储型、DOM 型，含 WAF/CSP 绕过与 sink 分析）。当目标把用户输入回显到 HTML/JS/属性/URL 上下文、或前端把可控数据喂给危险 sink（innerHTML/eval/srcdoc）时使用——典型场景：搜索/评论/标题回显、SVG/上传、postMessage、javascript:URL 伪协议、模板属性注入、绕 CSP。适用目标类型 Web 前端 / REST API。触发场景包括用户说"测下 XSS""这个参数会不会弹窗""能绕 CSP/WAF 吗""DOM XSS/存储型看一下"。输出：XSS 触发点 + 上下文 + PoC 的 finding（含 killed 记录）。

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Use when working with Neo4J — neo4j graph database analysis, index management, Cypher query optimization, APOC utilities, and cluster health monitoring.

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Guide for adult content payment processor selection, merchant account application, compliance, webhook integration, and fallback strategies (CCBill, SegPay, AllSecure, Epoch, Telegram Stars, crypto)

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Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

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GitHub Actions security review for workflow exploitation vulnerabilities. Use when asked to "review GitHub Actions", "audit workflows", "check CI security", "GHA security", "workflow security review", or review .github/workflows/ for pwn requests, expression injection, credential theft, and supply chain attacks. Exploitation-focused with concrete PoC scenarios.

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GPCR receptor pharmacology — agonist/antagonist/inverse-agonist/biased-agonist classification, GPCRdb structural data, receptor-ligand binding analysis, antibody-target interface (SAbDab). Use for GPCR drug discovery, biased-agonism analysis, receptor subtype selectivity questions, and orthosteric vs allosteric pocket characterization.

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Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.

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Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.

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End-to-end molecular docking pipeline. Target preparation, pocket detection, protein-ligand docking (DiffDock/Vina), scoring, interaction analysis, and pose ranking.

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Facilitate Kimball-style dimensional (data warehouse / BI) modeling sessions through conversation, producing DimML (Dimensional Modeling Language, YAML) as the primary artifact plus a bus-matrix + star-schema HTML. Use whenever the user wants to design fact tables, dimensions, a star schema, or a bus matrix — declaring grain, choosing SCD types, identifying conformed dimensions and measures (additivity) — by grilling one step at a time. Also invoke for refining an existing DimML, or when the user says "ディメンショナル・モデリング", "ディメンションモデリング", "スタースキーマ", "バスマトリクス", "ファクト表を設計したい", "グレインを決めたい", "DimML を育てたい", "Kimball で整理したい".

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Shakes your mental models with critical rigor, challenges assumptions, exposes blind spots, and provides multi-perspective insights as a strict thinking mentor. Deliberately breaks format conventions to stimulate genuine cognitive discomfort. Use when you want to stress-test an idea, shake your mental model, ask to challenge my assumptions, find blind spots, deepen my thinking, or request critical review with phrases like yusaburi or what do you think of this plan.

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Facilitate DDD domain modeling sessions via EventStorming conversation and DML (Domain Modeling Language). Use whenever the user wants to model a business domain by discovering domain events, commands, aggregates, policies, read models, and bounded contexts through dialogue. Produces incrementally-built DML as the primary artifact, plus a Markdown session report. Also invoke for refining existing DML, mapping out a new feature's domain model, or when the user says "ドメインモデリングしたい", "イベントストーミング", "DDDで整理したい", "DMLを育てたい".

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