51 件 ・ 関連度順
概要と使いどころ
Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.
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XSS attack prevention with input sanitization, output encoding, Content Security Policy. Use for user-generated content, rich text editors, web application security, or encountering stored XSS, reflected XSS, DOM manipulation, script injection errors.
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。
Think and work like an expert Cheminformatician. Use when a task calls for Cheminformatician judgment. Reasons from Standard InChI identity and RDKit sanitization through ChEMBL pChEMBL harmonization, Morgan/ECFP fingerprints, scaffold splits and applicability domains, while treating tautomer collapse, KNIME-vs-Python canonicalization drift, and random-split QSAR leakage as first-class failure modes.
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。
Expert guide for defending AI applications against prompt injection, jailbreak attacks, indirect injection via tool outputs, and data exfiltration through tool calls — input sanitization, output filtering, canary token detection, and layered defense architecture / Panduan ahli pertahanan aplikasi AI terhadap prompt injection, serangan jailbreak, injeksi tidak langsung via output tool, dan eksfiltrasi data melalui tool calls — sanitasi input, filtering output, deteksi canary token, dan arsitektur pertahanan berlapis.
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Execute comprehensive client-side injection vulnerability assessments on web applications to identify XSS and HTML injection flaws, demonstrate exploitation techniques for session hijacking and credential theft, and validate input sanitization and output encoding mechanisms.
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。
Render templates by replacing {{TOKEN}} placeholders with actual values, supporting all three templates (specification, plan, tasks) with schema validation and security sanitization
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Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.
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。
DOM-based XSS occurs when JavaScript reads attacker-controlled sources (`location.hash`, `document.referrer`, `window.name`, `location.search`) and passes them to dangerous sinks (`document.write`, `innerHTML`, `eval`, `location.href`, `setTimeout`, `jQuery.html()`) without sanitization. Unlike reflected/stored XSS, payloads never reach the server. Detect by auditing JavaScript for tainted data flow from DOM sources to sinks. Tools: Burp Suite DOM Invader, Chrome DevTools, DOMPurify (fix).
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。
Secrets management, XSS prevention, CSRF protection, dependency scanning, DOMPurify sanitization, CSP headers, CODEOWNERS, HttpOnly cookies
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。
The TypeScript target-language layer for Skmtc generators (@skmtc/lang-typescript): projection base factories, TsSnippet, the three register shapes, identifier kinds and the type-only import machinery, composition helpers, sanitization, TsFile render rules. Use ALONGSIDE skmtc-generator whenever a generator emits TypeScript. Headings are the template for other skmtc-lang-* skills.
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。
The Kotlin target-language layer for Skmtc generators (@skmtc/lang-kotlin): base factories, KtSnippet, the seven entity kinds, packages-from-paths imports, the head+value render model, KtAnnotation and the composition classes, sanitization and @SerialName placement, plus the current-API worked example (the shipped gen-kotlin-* packages are API-stale — do not copy their call shapes). Use ALONGSIDE skmtc-generator whenever a generator emits Kotlin. Headings mirror skmtc-lang-typescript.
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。
Cross-stack security review — auditing vulnerabilities across infrastructure, smart contracts, CI/CD pipelines, and AI agent systems, plus auth/sessions/crypto and validating user input at API and infrastructure boundaries. Triggers on mentions of "vulnerability", "pentest", "OWASP", "access control", "injection", "CSRF", "JWT", "smart contract audit", "supply chain", "OIDC", or any review of security-sensitive code paths spanning more than one layer. For PII sanitization see [security](../security/SKILL.md).
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。
Execute comprehensive SQL injection vulnerability assessments on web applications to identify database security flaws, demonstrate exploitation techniques, and validate input sanitization mechanisms.
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 applying security best practices — authentication, authorization, CSRF protection, input sanitization, rate limiting, or secure coding — stack-agnostic.
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。
Load this skill whenever the project contains SVG graphics — inline SVGs, external SVG files, SVG icons, SVG illustrations, or SVG-based data visualizations. Under no circumstances use SVG without proper accessible titles, descriptions, and ARIA roles where required. Absolutely always add <title> and <desc> to meaningful SVGs and set role="img" with aria-labelledby pointing to those elements. Also apply this skill whenever the project accepts untrusted or user-uploaded SVG content, which requires sanitization before rendering.
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。
Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.
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。
Audits WordPress plugin or theme PHP code for the most common security mistakes — missing nonce checks, capability checks, input sanitization, output escaping, unslashing, SQL preparation, AJAX nopriv exposure, file/path traversal, and unsafe redirects. Use when reviewing pull requests, before releasing a plugin, when the user asks "is this secure", or when handling code that touches $_GET / $_POST / $_REQUEST / $_COOKIE / $_FILES / $_SERVER, admin-ajax / admin-post, REST endpoints, options, user meta, custom DB queries, or file uploads.
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。
Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.
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。
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
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。
Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
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。
Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
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。
Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
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。
You are a frontend security specialist focusing on Cross-Site Scripting (XSS) vulnerability detection and prevention. Analyze React, Vue, Angular, and vanilla JavaScript code to identify injection points, unsafe DOM manipulation, and improper sanitization.
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。
Expert in secure frontend coding practices specializing in XSS prevention, output sanitization, and client-side security patterns. Use PROACTIVELY for frontend security implementations or client-side security code reviews.
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。