「retrieval」の検索結果

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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。

Interruption pattern detection and retrieval from Amp thread history. Use for analyzing tool rejection patterns and improving agent behavior.

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。

Anna's Archive integration for academic paper and book retrieval. Search shadow libraries (Library Genesis, Z-Library, Sci-Hub) via unified API with GF(3) balanced caching.

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。

Semantic codebase search, code indexing, and prompt enhancement via standalone CLI. Use when: (1) Semantic code search with natural language queries, (2) Code indexing for remote codebase retrieval, (3) Prompt enhancement with codebase context, (4) Before grep/find/glob operations for better accuracy, (5) Complex requirements clarification, (6) Large codebase navigation. Triggers: "search context", "enhance prompt", "find code that", "index project", "clarify requirements". IMPORTANT: Always use ace-tool BEFORE grep/find/glob for semantic-level code location.

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。

High-precision semantic search and content retrieval via Exa API. Use when: (1) Deep research requiring semantic understanding, (2) Code documentation and examples lookup, (3) Company/professional research, (4) AI-powered comprehensive research tasks, (5) URL content extraction with structured output. Triggers: "research", "find papers", "code examples", "company info", "LinkedIn profiles", "deep analysis". Differentiator: Exa excels at semantic/neural search while grok-search is better for real-time news and general web content.

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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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。

Expert context manager specializing in information storage, retrieval, and synchronization across multi-agent systems. Masters state management, version control, and data lifecycle with focus on ensuring consistency, accessibility, and performance at scale.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AI-agent, prompt-injection, MCP or toolchain, cloud, container, CI/CD, and supply-chain challenges. Use when the user asks to analyze prompt-to-tool flows, retrieval poisoning, mounted secrets, deployment drift, runtime-vs-manifest mismatches, registry provenance, or CI-produced artifacts under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Optional power-up — detects oversized codebases/docs and suggests tobi/qmd for local semantic search. Bridges cm-continuity (working memory) with long-term document retrieval. Zero-config detection, non-intrusive suggestion.

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 Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

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。

Nature-style remote-sensing manuscript reviewer skill for evaluating Earth-observation products, retrievals, machine-learning maps, validation design, uncertainty propagation, spatial transferability and claim-scope calibration.

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Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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 web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.

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 Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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。

Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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。

Retrieves full details of a specific Stitch screen — HTML download URL, screenshot URL, dimensions. This is the final step in design retrieval before code conversion.

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 auto-log all tool interactions into a self-organizing vector DB with instant recall. Intercepts interactions, extracts key information, generates embeddings, stores in LanceDB, auto-categorizes, and builds a retrieval index. Inspired by OpenClaw SQLite LCM + summary DAG pattern.

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 memory retrieval quality (drift, staleness, citation-groundedness) and produce remediation backlog.

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Use when you need semantic retrieval from perpetual memory at query time. Parses query intent, searches vector store, ranks by relevance and recency, and injects context. Complements memory-search.cjs with the perpetual_memory LanceDB table.

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。

Spaced repetition and retrieval practice engine for the vault. Use when the user wants a review session, recall practice, to test what they remember, or to resurface notes. Triggers on "review session", "what should I review", "recall practice", "resurface notes", "spaced repetition", "quiz me", "what do I know about", "inbox triage", "surprise connection".

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 Claude Project context budget under the automatic-RAG-by-window model: knowledge files vs. threshold-exempt overhead (Skills, MCPs, CI, Memory). Two modes: Quick Diagnostic and Full Budget Audit. Use when user says "check my context budget," "how much context am I using," "is my project too big," "optimize my token usage," "tier my files," "optimize for RAG," "improve retrieval quality," "should I keep compressing," "am I over-compressing," "should I accept RAG mode." Also trigger on context pressure symptoms: "Claude forgets my instructions," "responses getting generic," "content not found in my knowledge files." Also use when a project audit scores Knowledge Architecture ≤ 3. Do NOT use for content placement decisions (use rootnode-memory-optimization if available), full project audits (use rootnode-project-audit if available), or behavioral tuning (use rootnode-behavioral-tuning if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.

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 systematic knowledge acquisition from unfamiliar territory — deliberate model-building with feedback loops. Maps spaced repetition principles to AI reasoning: survey the territory, hypothesize structure, explore with probes, integrate findings, verify understanding, and consolidate for future retrieval. Use when encountering an unfamiliar codebase or domain, when a user asks about a topic requiring genuine investigation rather than recall, when multiple conflicting sources require building a coherent model, or when preparing to teach a topic and deep understanding is required first.

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Query the ChEMBL database web services for measured bioactivity data, compound records and calculated properties, targets, assays, mechanisms of action, drug indications and warnings. Use this skill to build curated SAR or QSAR datasets for a target, look compounds up by SMILES, InChIKey, name, or ChEMBL id, run similarity and substructure searches, and check what chemistry is already known against a protein. Also trigger when a query mentions ChEMBL ids (CHEMBL...), pChEMBL values, IC50/Ki/Kd/EC50 retrieval, assay confidence scores, or ebi.ac.uk/chembl.

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。