「ak sk」の検索結果

Execute plans via delegate_task subagents (2-stage review).

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。

p5.js sketches: gen art, shaders, interactive, 3D.

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。

Pitfalls, examples, and edge cases for FETIH Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.

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。

Pwn/Binary kategorisi SKILL.md — BOF, ROP, kernel exploit araçları kurma rehberi

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。

Rev kategorisi SKILL.md — Binary analysis, disassembly, symbolic execution araçları kurma rehberi

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。

Framer Motion React animation library skill — declarative animations, gestures, layout transitions, scroll-triggered animations, variants, AnimatePresence, and performance optimization.

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。

Crypto kategori SKILL.md — RSA, ECC, AES, hash, encoding saldırıları için gerekli araçları kurma rehberi

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。

Web kategorisi SKILL.md — SQLi, XSS, SSRF tarayıcı ve exploit araçları kurma rehberi

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。

Throwaway HTML mockups: 2-3 design variants to compare.

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。

Decomposition playbook + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.

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。

OSINT kategorisi SKILL.md — Kullanıcı araştırması, sosyal medya, subdomain keşfi araçları kurma rehberi

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。

Fetch Airbnb income estimate (ADR, occupancy, monthly revenue) for any area using the official Airbnb GraphQL API — the same data the Airbnb host simulator shows. Input: station/area name. Output: per-night rate, avg nights, monthly estimate for entire_home and private_room.

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。

Explain and list Peephole sec-debt markers — deliberate, human-authorized security trade-offs recorded in code. Use when the user says /sec-debt, wants to knowingly accept a security finding, or asks what deferred security items exist.

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。

Review the current changes for the CWE classes Peephole enforces, as a checklist including the absence-of-guard classes regexes cannot catch. Use when the user says /sec-review or asks for a security review of recent code before committing.

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。

Print Peephole's security audit report for this project — current mode, binary integrity, the CWE-mapped decision tally, sec-debt items needing approval, and tamper-evident log-chain status. Use when the user says /sec-audit or asks what Peephole has blocked or flagged.

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。

Builds multi-touch pipeline attribution models and generates stakeholder-ready narratives from campaign performance data. Use when analyzing channel contribution to pipeline, preparing monthly/quarterly revenue reports for leadership, diagnosing pipeline misses, modeling budget reallocation scenarios, or translating raw attribution data into board-deck language. Designed for B2B SaaS demand gen teams that report on pipeline and revenue, not leads or MQLs.

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。

Produces and audits externally published marketing copy where factual claims must survive scrutiny — landing pages, product pages, launch emails, one-pagers, search/social metadata. Every statistic, competitor comparison, capability description, and proof point is traced to a fetched source or the project's own documents, labeled, and adversarially verified before it ships. Use when writing or rewriting high-stakes marketing copy; when copy must pass legal, compliance, brand, or claims review; when the audience is skeptical technical or executive buyers; when the company has little or no customer proof yet (pre-launch, zero customers); or when auditing existing copy for unsupported claims, invented capabilities, unverified statistics, or AI-slop language.

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。

Apply repeatable dialogue MA, Studio-based or automatic portrait reframing, overlay-safe rendering, and loudness/sync QA to an existing interview or talking-head rough cut. Use when the user asks to make speech easier to hear, perform MA, enlarge or rebalance a person in frame, crop or punch in an interview, automatically propose framing, or prepare a finishing preview before final approval.

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。

Detect compromised O365 and Google Workspace email accounts by analyzing inbox rule creation, suspicious sign-in locations, mail forwarding rules, and unusual API access patterns via Microsoft Graph and audit logs.

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 and analyzes Bluetooth Low Energy (BLE) security attacks including sniffing, replay attacks, GATT enumeration abuse, and Man-in-the-Middle interception. Uses Ubertooth One and nRF52840 sniffers for packet capture, the bleak Python library for GATT service enumeration, and crackle for BLE encryption cracking. Use when assessing IoT device BLE security, monitoring for BLE-based attacks on wireless infrastructure, or performing authorized BLE penetration testing. Activates for requests involving BLE security assessment, Ubertooth sniffing, GATT enumeration, or BLE replay detection.

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。

Container escape is a critical attack technique where an adversary breaks out of container isolation to access the host system or other containers. Detection involves monitoring for escape indicators

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 Azure Blob and ADLS storage accounts for public access exposure, weak or long-lived SAS tokens, missing encryption at rest, disabled HTTPS-only traffic, and outdated TLS versions using the azure-mgmt-storage Python SDK.

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。

TLS 1.3 (RFC 8446) is the latest version of the Transport Layer Security protocol, providing significant improvements over TLS 1.2 in both security and performance. It reduces handshake latency to 1-R

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。

Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.

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。