「rope」の検索結果

Generate CHANGELOG entries following conventional commits format with semantic versioning. Use when finalizing features to maintain proper change history.

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 PlayCanvas StandardMaterial needs custom shading its properties cannot express, such as stylized looks or vertex displacement, before writing a ShaderMaterial.

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。

Write custom Semgrep rules to identify organization-specific logic flaws, improper cryptography usage, or missing authorization checks during source code review. This skill focuses on moving beyond default rulesets to locate complex vulnerabilities.

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 advanced Man-in-the-Middle (MITM) NTLM Relay attacks specifically targeting environments where SMB Signing is purportedly enabled but improperly configured (e.g., exclusively required on Domain Controllers but NOT enforced universally on standard Workstations or File Servers). Utilize powerful tools like Responder and Impacket's NTLMRelayX to coercively capture NTLMv2 authentications (via LLMNR, WPAD, or Coercion methods like PetitPotam) and seamlessly relay them across the network identically yielding immediate Local Administrator command execution.

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。

Extract sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.

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。

Extract sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary testing.

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。

Exploit PHP object serialization vulnerabilities (Insecure Deserialization). This skill details how to identify unserialize() injection points and construct malicious serialized objects using Property Oriented Programming (POP) chains to achieve Remote Code Execution (RCE).

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。

Identify and exploit logic flaws in OAuth implementations, focusing specifically on the absence or improper validation of the `state` parameter, which leads to Cross-Site Request Forgery (CSRF) and account takeover (ATO).

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。

Exploit JSON Web Tokens (JWT) by implementing the 'None' algorithm attack. This skill details how to bypass authentication mechanisms when a server improperly accepts JWTs with the `alg` header set to `none`, allowing attackers to forge tokens without a valid signature.

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。

Identify and exploit SQL Injection vulnerabilities in Django applications, specifically focusing on edge cases involving raw querysets (`RawSQL`), improper use of `.extra()`, and poorly sanitized filters where Django's typical ORM protections are bypassed.

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。

Clickjacking playbook. Use when testing whether target pages can be framed, whether X-Frame-Options or CSP frame-ancestors are properly configured, and whether UI redress attacks can trigger sensitive actions.

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。

Identify and exploit Mass Assignment vulnerabilities in APIs. Use this skill when testing REST APIs or application forms that directly map user-supplied JSON or POST input to internal database objects. An attacker can inject undocumented variables (e.g., `is_admin`, `verified`) to illegally modify restricted properties.

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。

AI/LLM red-team assessment using the OWASP LLM Top 10 (2025) + OWASP AI Testing Guide (AITG v1, Nov 2025) frameworks, plus OWASP MCP Top 10 runtime testing for agentic/MCP targets. Tests prompt injection, jailbreaks, system prompt leakage, sensitive data extraction, excessive agency, improper output handling, model extraction, content bias, evasion, membership inference, MCP token exposure, MCP command injection, and more. Uses four tools in combination: FuzzyAI (single-turn jailbreak fuzzing), PyRIT (multi-turn orchestrated attacks), Garak (probe-based vulnerability scanning), and promptfoo (plugin-based red-team evaluation). Each tool covers different OWASP categories; running them together gives systematic coverage. Includes a conditional MCP reconnaissance phase and a post-access AI infrastructure phase (chained from /post-exploit). Produces: OWASP LLM Top 10 + AITG + MCP coverage matrix, findings per category, architecture diagram of the AI system, PoCs for confirmed exploits. Chains into /gh-export for issue filing.

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 this agent when you need to create a pathway summary for a gene after completing the main gene review. This agent should be invoked specifically to generate GENE-pathway.md files that summarize the gene's role in biological pathways with proper citations and mermaid diagrams.

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 this agent when you need to perform custom bioinformatics analyses on genes or proteins that go beyond standard database lookups. This includes situations where existing domain/family annotations from UniProt are insufficient, when you need to verify dubious annotations, or when you need to perform comparative analyses across gene sets. The agent creates reproducible analysis pipelines with proper dependency management and documentation.

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 styling PyQt/PySide6 widgets with QSS - selectors and pseudo-states, stylesheet application, common style properties, widget-specific styling, or building a dark theme

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 working with PyQt/PySide6 QtCore - signals, slots, properties, timers, settings, file I/O

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。

Frappe customization-surface guidance covering Custom Field, Property Setter, Client Script, Server Script, Workspace, Web Page, Page, Print Format, Report, Dashboard, Workflow, Role, Notification, Webhook, and related builder/admin DocTypes. Use when choosing or changing Frappe customization layers.

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。

Design token architecture - primitive/semantic/component tiers, naming grammar, CSS custom property delivery, and DTCG build pipelines

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。

Graph-relational database with EdgeQL query language, code-first schema, link-based relations, computed properties, and fully typed TypeScript query builder

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 this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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 and repair Gaia user-tree timelines so every skill's current rank is explained by a chain of Hero's Journey events. Trigger this skill when: a contributor's profile chart shows a stale rank (e.g. still shows 3★ after a demotion to 1★); someone asks to "trace what happened" to a skill's rank; `validate_timelines.py` is failing in CI; you need to backfill missing `demote` or `rank_up` events before cutting a release; a skill was reclassified or Star-Bar-reset and the user tree was never updated; or any time the registry node and user tree disagree on a skill's current level. Uses `scripts/trace_timeline.py` (dry-run + apply) and `scripts/validate_timelines.py` (CI gate). Do NOT hand-edit `timeline[]` arrays — the script synthesises properly labelled events with `previousValue`/`newValue` so the rank chart plots correctly.

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。

End-to-end plan → implement → review pipeline that takes one triaged Gaia issue to a merged-ready PR, with Kill Criteria on the issue and zero follow-up debt. Use when someone says: "resolve this issue", "take #N end-to-end", "work the P0 queue", "fix and ship this issue", "run the resolver", "close out this issue properly", or /gaia-issue-resolver. Works any gaia-research org repo — gaia-skill-tree, gaia-research, gaia-skill-heaven — while the issue itself lives on the headquarters tracker, gaia-research/gaia-skill-tree. Assumes the issue is already triaged and carries a P0–P4 label; if it does not, run /gaia-triage first.

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。

Multi-agent exploration-to-PR pipeline for safely exploring, fixing, and shipping a feature or CLI. Use when the user says: "explore X", "test and ship X", "run the pipeline on X", "run /feature-pipeline on X", "let's properly investigate X", "stress-test X and file issues", or "take X through the full pipeline." Also triggers on: "find what's broken in X", "test X end-to-end", "do a sandbox review of X", "file issues for X and fix them." Do NOT use for single-file bug fixes or one-off edits — the overhead is only justified when exploration, planning approval, and CI watching all matter. Manages state via scripts/state.sh (jq required). Five phases: LIGHTER explores → ORCHESTRATOR drafts PR → HEAVIER plans (user approves first) → ORCHESTRATOR commits → HEAVIER sandbox review → LIGHTER iterates CI → LIGHTER drift check + summary. Four mandatory stop hooks (M1–M4) gate each phase transition.

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。