「agent memory」の検索結果

Construa, depure e otimize aplicativos com Claude API / SDK Anthropic. Aplicativos construídos com esta skill devem incluir cache de prompt. Também lida com migração de código Claude API existente entre versões de modelo (4.5 → 4.6, 4.6 → 4.7, substituições de modelo descontinuado). TRIGGER quando: código importa `anthropic`/`@anthropic-ai/sdk`; usuário solicita Claude API, SDK Anthropic ou Managed Agents; usuário adiciona/modifica/afina um recurso Claude (cache, thinking, compaction, tool use, batch, files, citations, memory) ou modelo (Opus/Sonnet/Haiku) em um arquivo; dúvidas sobre prompt caching / cache hit rate em um projeto SDK Anthropic. SKIP: arquivo importa `openai`/SDK de outro provedor, nome de arquivo como `*-openai.py`/`*-generic.py`, código agnóstico de provedor, programação geral/ML.

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Manus-style file-based planning for multi-step work. Keep task_plan.md, findings.md, progress.md in your run workspace so the filesystem is your working memory. Use for any research/build with 5+ tool calls. Adapted for OpenMark sub-agents from Ahmad's planning-with-files v3.0.0.

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Pull version-current Convex docs for the version this project uses — pin the installed version, fetch page-as-markdown or check node_modules types, freshness hierarchy — instead of writing a possibly-stale API from memory.

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Secure Solana wallet generation using official Solana Labs libraries — Ed25519 keypairs, memory zeroization, vanity address generation, offline-only operation, and multi-language implementations (Rust, TypeScript, Bash).

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Educational single-threaded TypeScript vanity address generator for Solana using @solana/web3.js with async iteration, event-loop yielding, streaming generator API, and best-effort memory zeroization.

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Defense-in-depth security across Rust, TypeScript, and Bash for the Pump SDK — cryptographic key handling, memory zeroization, secure file I/O, input validation, privilege management, dependency auditing, and a 60+ item security checklist.

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Use when logging, scoring, and triaging a learner's persistent weak areas to drive intervention selection.

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Metakognitive Injektoren und Selbstgesprächs-Strategien (Self-Talk, Metacognitive Auditing, Evaluator Hooks, Pre-Flight Checklists, State Persistence & Personas). Integriert Aktive Informationssuche (Systemgedächtnis Gardener & USMC), Rehearsal (Aktives Abrufen & Wiedergeben), Baddeleys Arbeitsgedächtnis (State + Hooks), Miyake Exekutivfunktionen sowie CBT/ACT therapeutische Strategien.

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Onboard a new machine as a receive-only follower on a multi-machine agent network: it reads the shared vault, canon, skills and memory, runs nothing heavy locally (no RAG, reindex or GPU), and never receives executable hooks from the hub. Checklist-driven, with a verify step at each stage. Triggers: "/follower-onboard <machine>".

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Use tsift for token-efficient repository navigation, code search and reading, call graphs, diffs, logs, tests, session context, and workspace memory. TRIGGER: exploring or changing a codebase with tsift installed, or working on the tsift repo. SKIP: plain file-level glob, non-code web search. VERSION CHECK: compare `tsift --version` against tsift-version below before trusting any command text copied from this file.

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Complete WhatsApp management for OpenClaw agents: per-conversation memory (groups + DMs), unanswered message tracking, loop prevention, and multi-PA coordination. Use when: tracking conversation context, recalling past decisions, finding unanswered messages, or preventing echo/duplicate message loops.

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Evaluate everything the PA agent manages — tasks, skills, PA network health, billing, calendar connections, and memory quality. Use when: owner asks for an evaluation, wants to know what's working and what isn't, or requests a performance report. Combines supervisor status with quality scoring.

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Session stores conversation history for a specific session, allowing agents to maintain context without requiring explicit manual memory management.

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Serena MCP for project memory and code navigation. Use when managing Serena memories, navigating symbols, performing intelligent refactoring, or maintaining context/continuity across AI agent sessions.

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Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.

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Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.

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Set up the current project folder for AOS: slug, wiki, memory, Synapse map, AGENTS.md. Generated from plugin-commands.json; invoke as $bdb-aos:init.

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Change the agent harness itself (AOS, AO or CBS hooks, gates, memory, permissions, delegation, plugins, bootstrap) the safe way: look up the matching harness pattern, design against its gotchas, build with /startcycle and a live trail, check every harness, open a PR after GO. Use for "change a hook", "fix the go-gate", "harness work", "plugin change".

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Start a new private GitHub project the AOS way: create the private repo, bootstrap AOS (project card, AGENTS.md, wiki, memory, CI choice), push after GO, and confirm the first CI run is green and the repo is still private. Use for "new project", "start a repo", "bootstrap a project".

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Use when deep scan and ingest project files (.md, .json, agent.md, .openwiki) and past conversation logs into the local memB vector memory engine.

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Interview a project folder into AOS and write the result: a stable slug and domain in .aos/project.json, an OpenWiki wiki that is actually watched and does not index junk, memB memory bound to the slug, a Synapse map, AGENTS.md with its harness symlinks, and optionally CI and issue triage.

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Token budgeting discipline for all agents. Treats tokens as a resource from session start — not just when the buffer hits 60%. Covers prompt compression, context hygiene, handoff packing, and HOT memory pruning.

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Use when: Good night Team, EOD, Wrapping up, or end-of-day consolidation. Archives session context, promotes durable patterns, and compacts live agent memory files for the next session.

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Focused Signals scout for PostHog setup health. Reads the project's active health issues — the deterministic findings of PostHog's own health checks (no live events, outdated SDKs, missing reverse proxy, absent web vitals, ingestion warnings, failing data-warehouse models, and more) — and decides which are genuinely worth surfacing. Unlike a one-signal-per-issue push, it bundles kind-clusters into a single finding, weights by real blast radius (cross-referencing actual event volume and reach), and prioritizes issues an agent can resolve via the MCP. Emits only above the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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。