「chain」の検索結果

Bug-fix discipline — every defect has an etiology, the chain of cause that produced the observable fault; trace it before patching. Errors only grow: a bandaid leaks unless genuinely localized and leak-free. Walk the diagnostic ladder (presentation → proximate cause → API contract → isolated or systemic) before writing the fix. Use when authoring or reviewing any bug fix, regression patch, "quick fix" PR, or when deciding whether to ship, defer, or refactor.

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。

Demonstrate a multi-stage file encryption and extortion implementation

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。

Insecure file upload playbook. Use when testing upload validation, storage paths, processing pipelines, preview behavior, overwrite risks, and upload-to-RCE chains.

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。

Stack overflow and ROP playbook. Use when exploiting buffer overflows to hijack control flow via return address overwrite, ROP chains, ret2libc, ret2csu, ret2dlresolve, or SROP on Linux userland binaries.

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。

Linux kernel exploitation playbook. Use when exploiting kernel vulnerabilities (UAF, OOB, race condition, type confusion) for privilege escalation via commit_creds, modprobe_path overwrite, or kernel ROP chains in CTF and real-world 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。

Tunneling and pivoting playbook. Use when establishing network tunnels through compromised hosts including SSH tunneling, Chisel, Ligolo-ng, socat, DNS/ICMP/HTTP tunneling, ProxyChains, and multi-layer pivoting strategies.

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。

iOS pentesting playbook. Use when testing iOS applications for keychain extraction, URL scheme hijacking, Universal Links exploitation, runtime manipulation, binary protection analysis, data storage issues, and transport security bypass during authorized mobile security assessments.

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。

Entry P0 primary router and operating doctrine for HackSkills. Use when the task involves web application testing, API security assessment, recon, vulnerability triage, exploit path planning, authorized pentest, code audit, source-leak mining, middleware audit, SOC triage, detection engineering, incident response, or choosing the right next category skill before any deep topic skill. Also use when the user mentions 安全工程师, 渗透测试, 红队, 蓝队, 代码审计, 源码泄露, SRC. Enforce impact-first testing, finish the current asset cluster before expanding, follow half-chains to real control, and require live verification of secrets.

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/ML security playbook. Use when assessing model supply chain attacks (pickle RCE, poisoned weights), adversarial examples, model poisoning, model stealing, data privacy attacks (membership inference, model inversion), and autonomous agent security risks.

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。

Diagnose and fix retention problems using behavior design (B=MAP). Use when the user mentions "users sign up but dont stick around", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users quit after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, see drive-motivation.

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 and build an official Apify integration for a company's product - workflow-automation apps (Zapier/n8n-style), AI agent plugins (coding-agent skills+MCP bundles or OpenClaw/Hermes-style harnesses), AI framework packages (LangChain/LlamaIndex-style), or direct application clients via apify-client. Use when planning, creating, or reviewing an integration that exposes Apify Actors, runs, datasets, or key-value stores inside another product.

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 update Pilot Shell upstream dependencies in installer/upstreams.yaml. Checks npm, PyPI, brew, and curl-pipe entries for newer stable versions, known security advisories, and sha256 drift. Verifies each update installs successfully before committing. Use when: "check upstreams", "update dependencies", "audit upstreams.yaml", "are our dependencies up to date", "supply chain check".

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。

Every shot carries its own timecode and shows one visible change on a body. The dread comes from the chain — a look, veins, a bite, the next person — and the ending seals a door without settling anything. Use when the user wants a Seedance 2.5 / 2.0 video prompt for this kind of clip (Horror and suspense; 中文触发词: 恐怖悬疑短片, 恐怖, 悬疑, 惊悚), or asks how published cases of this type were prompted. Read references/cases.md before drafting.

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。

Manages the flow of goods and inputs — sourcing, inventory, lead times, fulfillment, and supply risk. Use this to reduce stockouts or excess inventory, plan for a supplier failure, decide reorder points and safety stock, improve fulfillment reliability, or assess concentration risk in a supply base.

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。

Owns execution: how work actually gets done across the organization, including process, program management, capacity, vendors, supply chain, and service delivery. Use this when execution is the problem rather than strategy, to design or fix a process, to resolve cross-functional handoff failures, to plan capacity, to assess delivery risk, or when the same failure keeps recurring. Also use to decide whether to build, hire, or outsource a capability.

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。

Generate AI videos using ByteDance Seedance models via Volcengine Ark API. Supports text-to-video, image-to-video (first frame, first+last frame), multimodal reference (images+videos+audio), video editing, video extension, web search enhancement, audio generation, draft mode, offline inference, and continuous video chaining. Use when user wants to generate, create, edit, or extend AI videos from text prompts, images, videos, or audio.

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。

Run the CI gate on your machine so it agrees with the runner — deriving the exact command, paths, markers, and env from the workflow file instead of the Makefile, unblocking gate steps that short-circuit and hide the next failure, pinning the linter version CI resolves, building the interpreter/toolchain environment the runner builds, and confirming the run is green instead of explaining a red job away. Use when a check passes locally but fails in CI (or the reverse), when a lint/format job goes red on an untouched file, when setting up a local dev loop for an unfamiliar repo, or before pushing a branch you expect to merge.

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。

Verifies whether a scientific or biomedical claim is actually supported by the cited original papers rather than by citation drift, overstatement, selective citation, or correlation-to-causation inflation. Use this skill whenever a user wants to check whether a repeated statement, slide claim, manuscript sentence, review assertion, or “people often say” scientific conclusion is truly supported by the underlying primary literature. Always separate the claim itself, the cited paper(s), what the paper actually showed, what it did not show, and whether later retellings drifted beyond the original evidence. Never fabricate references, findings, study features, or citation chains.

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。

Assesses whether study results are trustworthy by auditing design integrity, sample structure, statistical handling, bias control, validation chain, and claim discipline. It identifies where results are robust, fragile, overfit, under-validated, or overclaimed. Always separate reported findings from reliability judgment. Never fabricate references, PMIDs, DOIs, trial identifiers, study features, or validation claims.

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。

Explains why studies on the same biomedical topic reach different or opposing conclusions by auditing differences in population, endpoint definition, sample source, assay or platform, study design, statistical model, adjustment strategy, validation chain, and bias control. It separates true contradiction from apparent contradiction caused by framing or methods. Never fabricate references, PMIDs, DOIs, trial identifiers, dataset details, platform details, study features, or conflict explanations that are not supported by the input.

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。

Systematically maps mechanism evidence for a disease from molecules to pathways, cell types, tissues, biological consequences, and clinical phenotypes. Always use this skill when a user needs a layered mechanism evidence chain rather than a flat summary or immediate gap analysis. Formal literature citations must be real and verifiable.

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 evaluating US stocks through Serenity / @aleabitoreddit's AI and semiconductor supply-chain lens: upstream chokepoints, photonics/CPO bottlenecks, hyperscaler capex, institutional rotation, dilution risk, and thesis validation.

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 evaluating crypto investments through a Multicoin-style thesis lens: high-conviction sector bets, performance chains, token value capture, and crypto-native market structure.

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 evaluating Hindenburg-style investigative short reports: evidence chains, promoter networks, stock promotion, related parties, disclosure failures, and catalyst-driven short narratives.

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。