「understand」の検索結果

This repo is indexed by graft/. For ANY task here, whether understanding how something works, finding where code lives, tracing what calls a symbol or what a change breaks, or scoping an edit, get your context from the graph before grepping or reading source files. Every command runs through `bin/harness graft`.

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。

Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".

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。

Understand audience demographics, preferences, behavior patterns, and engagement quality across Facebook, Instagram, YouTube, and TikTok.

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 know AI wrappers get a bad rap, but the good ones solve real problems. You build products where AI is the engine, not the gimmick. You understand prompt engineering is product development. You balance costs with user experience. You create AI products people actually pay for and use daily.

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。

A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.

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 cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.

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。

I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.

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。

Plan, run, and measure an adversary engagement operation using the MITRE Engage framework so that deployed deception is driven by strategy instead of deployed ad hoc. Covers the Engage Matrix (Prepare, Expose, Affect, Elicit, Understand), the 10-Step Operational Process, mapping engagement Activities to the ATT&CK techniques they expose, and defining measurable Goals and Operational Objectives. Use when a team has honeypots, honeytokens, or canary tokens but no coordinating strategy, when leadership asks "should we engage attackers and how", when building a deception/denial program, when writing an adversary engagement operation plan, or when deciding which deception Activities to deploy against a specific threat actor. Keywords: MITRE Engage, adversary engagement, cyber deception strategy, denial and deception, Engage Matrix, EAC, EGO, Expose Affect Elicit, deception program, honeypot strategy, engagement operation.

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。

This skill outlines methodologies for performing authorized penetration testing against AWS, Azure, and GCP cloud environments. It covers understanding the shared responsibility model for testing scope, leveraging cloud-specific attack tools like Pacu and ScoutSuite, exploiting IAM misconfigurations, testing for SSRF to cloud metadata services, and reporting findings aligned to MITRE ATT&CK Cloud matrix.

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。

Analyzes malware command-and-control (C2) communication protocols to understand beacon patterns, command structures, data encoding, and infrastructure. Covers HTTP, HTTPS, DNS, and custom protocol C2 analysis for detection development and threat intelligence. Activates for requests involving C2 analysis, beacon detection, C2 protocol reverse engineering, or command-and-control infrastructure mapping.

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。

Check upgrade safety for a Tamanu upgrade that spans many versions: verify that every intermediate hotfix is included in the target version, and surface the new configuration/settings, data migrations, and FHIR rematerialisation impact the upgrade brings. Use when the user wants to check an upgrade from one Tamanu version to another (e.g. "check hotfixes from v2.31 to v2.47"), confirm no intermediate hotfix was dropped, or understand what config, migrations, and FHIR rework a multi-version jump requires. Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for cutting a release (see release-cutoff-checks) or deciding which tests to run (see scope-tamanu-release-tests).

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。

Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical 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。

Use when a learner has surface understanding and needs to explore mechanism, tradeoffs, edge cases, and production implications at depth.

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 learner asks to learn from a repository and the agent must map curriculum structure and prerequisites before teaching.

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 onboarding documentation for a codebase — architecture overview, key files, local setup, and common gotchas. Use this skill whenever someone asks to onboard onto a codebase, create onboarding docs, generate a codebase overview, explain a project's architecture to newcomers, or asks "how does this codebase work". Also trigger when users say things like "document this repo for new devs", "write a getting started guide", or "help me understand this project".

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。

Build a rich, self-contained HTML explanation of a code change — diff, branch, commit range, MR/PR — with background, intuition, a code walkthrough, diagrams, and a glossary, then quiz the reader interactively in chat. Use when the user asks to explain a change, understand a PR/MR, walk through a diff, onboard onto a branch, or says "explain this PR", "what does this branch do", "teach me this change", or "/explain-diff".

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 explore an unfamiliar codebase and build a reusable architecture summary. Use for repo onboarding, understanding legacy or inherited code, mapping architecture before a migration or refactor, tracing a bug or feature path, or answering "what does this project do?". Documentation-first, then data flow — ends in a concise technical architecture summary with file:line evidence, not a raw file dump.

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。

Compile deep repository evidence into artifact-bound correctness doctrine — authority/law/proof maps, owned invariants, failure archaeology, and the strongest durable destination for each piece of knowledge, plus an optional minimal repository-specific skill portfolio. Use when the user wants both deep codebase understanding AND durable doctrine, knowledge routing, or repo-specific skill recommendations. Research discoverable facts before asking. Read-only. Not for quick onboarding, one isolated invariant, ordinary implementation, generic review, or direct skill creation.

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。

Interview the user relentlessly about a plan or design until reaching shared understanding. Use when fleshing out an idea, stress-testing a design, or when the user says "grill me".

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。

Crystallize shared understanding into a reviewable spec, or update an existing one. Use when the problem needs a written specification, when assumptions or decisions have changed, or when the user says 'write a spec'.

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 current repo structure, build system, test setup, and conventions to provide a practical onboarding guide. Use when new to a codebase, joining a project, or wanting to understand how a repo is organized. Triggers on getting started, new to repo, onboard, how does this repo work, repo structure, codebase overview.

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 a DRAFT quiz from the training content the user supplies (slide deck, manual, e-learning script, session transcript, procedure): numbered questions with the correct answer, distractors drawn from the content itself, an explanation quoting the source passage with its location, a difficulty tag (Recall, Apply, Analyse) with its basis, and a coverage table showing which sections and objectives each question tests and which are left uncovered. Use when the user asks to "build a quiz from this deck", "write knowledge check questions for this module", "create an assessment from this training", "test understanding of this procedure" or "which sections does this quiz cover". Do not use for a reader-facing question and answer page built from documents, use faq-builder instead; for writing the procedure itself from notes or a walkthrough, use sop-drafter. Drafts for human review; never approves, authorises or signs off.

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。

Read and understand PDF documents with the pdf_* tools - inspect a document before reading it, extract text in reading order or as reconstructed layout, search a document without reading all of it, recognize scanned pages that have no text layer, and render pages as pictures. Covers scanned documents, chat attachments, page-range and character caps, and the read-only and untrusted-content rules.

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。

Tutor the user step by step on a plan, implementation, codebase, or topic to pay down understanding debt left by AI tooling. Explicit invocation only.

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。