「iga」の検索結果

Quickly search and navigate the codebase - Use when you need to quickly find files, search for patterns, or locate specific code without deep analysis.

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。

Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, agent browsing, research. Triggers: browser, web automation, scrape, navigate, click, fill form, screenshot, browse web, playwright, headless browser, web agent, surf internet, record video

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。

Screen a Medicare/Medicaid claims corpus for fraud, waste, and abuse and produce ranked, fully-cited investigation referrals for an SIU / program-integrity team. Use when asked to run a fraud sweep, screen claims for FWA, find billing anomalies, or generate investigation referrals over a claims dataset.

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Gather and correlate open source intelligence from public sources for authorized investigations, threat intelligence, and attack surface assessment. Use when the user mentions 'OSINT,' 'open source intelligence,' 'digital footprint,' 'public records,' 'threat intelligence,' 'investigate a domain,' or needs to research a target using publicly available data.

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Triage a single security finding — from a scanner, audit, advisory, or report — to a defensible disposition with a mitigation plan, false-positive justification, or accepted-risk writeup. Use when the user mentions 'triage this finding,' 'is this a real vulnerability,' 'mitigation plan,' 'false positive,' 'accept this risk,' 'compensating controls,' 'risk justification,' 'security ticket,' 'CVSS this,' 'should we fix this,' 'disposition,' 'sign off on,' or has a single security finding and needs to decide what to do.

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。

Deploy non-violence as navigation itself - Ahimsa, Anekantavada, Syadvada active.

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Choose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via claudish, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.

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Use when bypassing a Windows exploit/platform mitigation — ASLR/DEP/CFG/XFG/CET, ACG/CIG, WDAC/App Control, ASR/AMSI/ETW, PPL/LSA Protection, BYOVD/VBS/HVCI

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Query IUPHAR/BPS Guide to Pharmacology (GtoPdb) for receptor-ligand interactions, target/ligand metadata, families, and approved drugs. Affinities (pKi/pIC50/pKd), action (Agonist/Antagonist/etc.), species, structures (SMILES/InChI). No auth. Always resolve targets via geneSymbol/accession; most metadata lives in sub-resources (/databaseLinks, /structure, /synonyms).

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Diffusion-based docking that predicts protein-ligand poses without a predefined site. Use for blind docking, when traditional docking fails, or exploring multiple binding modes. Pipeline: prep protein (PDB) and ligand (SMILES/SDF), run inference, analyze confidence-ranked poses.

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This skill should be used when the user asks to determine what tax filings they need, wants to know if they must file a tax return (確定申告), asks about consumption tax obligations (消費税), or needs help understanding their filing requirements. Trigger phrases include: "確定申告が必要か", "申告の種類", "消費税の届出", "課税事業者かどうか", "何を申告すればいい", "申告要否", "税金の申告", "住民税の申告".

Use when facing 2+ independent investigations that can proceed without shared state - parallel literature survey, multi-dataset replication, or pre-specified robustness checks

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API reference: SwiftUI. Query for views, layouts, navigation, @State/@Binding/@Observable, view modifiers, NavigationStack, iOS 26+ features.

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GDPR Article 28(2) sub-processor approval workflow management. Covers prior specific and general authorization mechanisms, change notification procedures, objection windows, flow-down obligation enforcement, and sub-processor chain risk monitoring.

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Conduct LINDDUN privacy threat modeling across all seven categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat trees, privacy-specific mitigation strategies, and integration with STRIDE security threat modeling.

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Complete CCPA/CPRA compliance implementation covering California Civil Code §1798.100-199. Includes consumer rights framework, business obligations, service provider and contractor requirements, enforcement mechanisms, and CPPA rulemaking. Triggers on CCPA, CPRA, California privacy, consumer rights.

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Implements CCPA Section 1798.105 right to delete and CPRA amendments including service provider obligations, statutory exceptions for legal, security, and internal uses, consumer identity verification procedures, and 45-day response timeline management. Activate for CCPA deletion, CPRA right to delete, California privacy, consumer deletion queries.

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Evidence-based investigation of issues, bugs, API calls, integrations, and other aspects of software development that need a deep dive to find the root cause and solutions. Use when you need to debug, troubleshoot, diagnose, or figure out why something is broken. Does not review code for quality or style — use code-review for auditing changes or post-code-review-to-pr for posting review feedback to GitHub. Does not assess architectural health or structural risk — use architectural-analysis for architectural concerns. Does not research open-ended options, prior art, or how something works when nothing is broken — use research for that. Does not plan the structural change a root cause calls for — use plan-a-change. Does not map bounded contexts or domain boundaries — use ddd-analysis. Does not capture feedback on Han's own skills — use han-feedback for that.

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Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for planning the change its findings imply — use plan-a-change. Not for discovering bounded contexts, ubiquitous language, or where code boundaries diverge from domain boundaries — use ddd-analysis. Not for writing documentation or architectural decision records.

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Performs a gap analysis between two artifacts (a current state and a desired state) and produces a plain-language, stakeholder-readable report indexed by stable gap IDs. Use when the user wants to compare, evaluate, audit, or reconcile one artifact against another. Does not investigate runtime bugs — use investigate. Does not assess module-level architecture — use architectural-analysis. Does not research open-ended options with no second artifact to compare against — use research.

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Capture structured feedback on the Han skills and agents used in the current session and optionally post it as a GitHub issue to testdouble/han. Use at the end of any session where one or more han-* skills or agents ran, to rate a run, log what worked and what didn't, or submit observations for maintainers. Does not review code, investigate bugs, or research options; use code-review, investigate, or research for those. Does not provide feedback on skills or agents from non-Han plugins.

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。

Conduct deep web research using the openbrowser-ai agent: decompose a query, investigate sub-questions across multiple sources, and produce a cited markdown report plus structured JSON under local_docs/research/. Trigger when the user asks to: research a topic, do a deep dive, investigate, gather evidence, compare options, write a literature review, build a briefing, or produce a cited report.

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。

Fix ClickHouse query errors in Rust when using clickhouse-rs crate. Use when: (1) "string is not valid utf8" errors - typically FixedString columns need CAST to String, (2) "tag for enum is not valid" errors - typically Option<T> fields receiving non-NULL values, (3) Sum/count aggregations returning Float64 but Rust expects u64, (4) LEFT JOIN results with empty strings where Rust expects Option::None, (5) "not enough data, probably a row type mismatches a database schema" - query SELECT returns fewer columns than the Rust Row struct expects (common when multiple query functions share the same struct but one function is missing columns added later), (6) Garbled/corrupted data when INSERTing to FixedString(N) columns using String Rust type - silently corrupts data (no error!) because String adds a length prefix but FixedString expects exactly N raw bytes. Fix: use [u8; N] with #[serde(with = "BigArray")] from serde-big-array, (7) "the trait bound `[u8; 64]: serde::Serialize` is not satisfied" when using [u8; N] arrays in Row structs - need #[serde(with = "BigArray")] annotation from serde-big-array crate.

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 you need to organize and structure site navigation.

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。