「memo」の検索結果

AgentDB Persistent Memory Patterns operates on 3 fundamental principles:

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 hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

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Git Context Controller (GCC) v2 — Lean agent memory backed by real git. Stores hash + intent + optional decision notes instead of verbose markdown. Auto-bridges to aiyoucli vector memory when available. Dual mode: git-backed (lean index.yaml) or standalone (markdown fallback). Triggers on /gcc commands or natural language like 'commit this progress', 'branch to try an alternative', 'merge results', 'recover context'.

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What this project has already learned the hard way, and where any new such fact belongs. Two jobs. (1) Routing: this project keeps NO per-project Claude memories — read this before writing anything down, and pick a durable home from the list here. (2) An index, by area, of non-obvious facts worth knowing before you guess: debugging rigs and measurements that lie, iOS/WebKit and the tethered iPhone, the Windows app, production and deployment, and general codebase traps. Use before setting up a browser- or server-driven debugging session, when a bug reproduces only on iPhone, in Safari or in the Windows app, before changing deployment configuration or reading store/crash data, when something behaves unexpectedly and you are about to guess why, and whenever you are tempted to record a fact for later.

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Design system that makes HTML output look like a professional consulting deliverable instead of default AI styling. Use WHENEVER generating any HTML document — report, analysis memo, proposal, research summary, dashboard, artifact — and when the user says 「HTMLで資料」「HTMLでまとめて」「レポートにして」「コンサル風に」「資料っぽく」「デザインがダサい」 or asks to restyle existing AI-generated HTML. Replaces boxes, borders, gradients, emoji and rainbow colors with whitespace-driven, typography-first, print-ready (A4) design.

Inspect repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.

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。

Inspect repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.

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 prompt-injection, retrieval poisoning, memory contamination, planner drift, MCP or tool-boundary abuse, and agent exfiltration challenges. Use when the user asks to analyze prompt injection, retrieval poisoning, memory contamination, planner drift, tool-argument corruption, or secret exposure caused by an agent chain. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Working memory protocol — routes to session protocol, memory audit, MCP/URI integration, or storage details based on the current need. Keeps default continuity usage cheap.

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。

Inspect repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.

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。

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every 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。

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

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Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

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Inspect captured RAM images to enumerate processes, modules, handles, and suspicious in-memory behavior before escalation or evidence handoff.

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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。

Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

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Use when keeping imp's docs and config examples coherent after a change - updating docs/internals/ARCHITECTURE.md / README / docs/GOAL.md / docs/MODELS.md / docs/roadmap.md / imp.conf.example / CHANGELOG / MISSION_JOURNAL, or "is this doc stale", "document this change", "the example config is out of date", "the README says X but the code does Y", "add a roadmap ledger row". Do NOT use for layer/frontmatter/lint questions (docs-layers), structural code audits (codebase-audit), measuring perf or refreshing the perf baseline (benchmark-cuda), or the agent's private memory (MEMORY.md).

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Creates VC IC presentation materials with deal thesis, market analysis, team assessment, competitive landscape, and return scenarios. Use when preparing IC memos, presenting investment opportunities, or documenting investment decisions.

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Creates debt offering memoranda with credit overview, financial analysis, industry section, and risk factors for investor marketing. Use when preparing debt OMs, writing credit marketing materials, or structuring lender presentations.

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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Records atomic learned behaviors with confidence scores. Project-scoped instincts are isolated per project and auto-promote to global scope at confidence threshold 0.8. Stores instincts in .claude/context/memory/instincts.jsonl

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Feed actual task results back into agent memory for calibration. Compares predicted vs actual outcomes, records accuracy scores, and tracks estimation quality, prediction quality, and decision quality over time to improve future agent performance.

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Scheduled ArXiv paper monitor. Uses CronCreate to search configured keywords every 6 hours, deduplicates via MemoryRecord, and stores summaries in named memory for morning briefing integration.

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 should be used when the user wants to build, set up, or operate an AI "second brain" — a persistent memory vault (Obsidian-compatible Markdown) that lets an AI agent resume work without re-explaining context, remembers correction instructions across sessions, and auto-organizes raw material into structured knowledge. Trigger on requests like "build my second brain", "set up an Obsidian vault for AI", "give my AI persistent memory", or "make AI stop forgetting".

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。