「memory」の検索結果

Sourdough and artisan bread expert covering fermentation, scoring, and professional baking techniques

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Expert in formal logic, model theory, computability, and foundations of mathematics

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。

Structured multi-hop web research with explicit confidence gating — plan the inquiry, search (WebSearch/WebFetch), score your own confidence, and keep digging until the answer is well-supported or a hop cap is hit, then emit a cited synthesis. Learns across sessions by storing each research case to memory and reusing prior strategies. Use when a question needs more than one search — comparisons, current-best-practice questions, anything where a single lookup leaves you unsure.

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。

Deliberate session retrospective — look back over what you just did, distill the durable, reusable lessons (not session trivia), and write them to the learnings memory namespace, deduped against what is already stored. Use at the END of a meaningful chunk of work to capture high-signal lessons worth keeping long-term. The curated counterpart to moflo's passive session-continuity capture.

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。

Verify-before-done — exercise the current change end-to-end against its acceptance criteria (the SDD plan's, or the ticket's) and report a per-criterion PASS/FAIL verdict, then record the outcome to memory. This is the concrete action that satisfies moflo's verify-before-done gate (`gates.verify_before_done` / `/flo -v` / `/flo -sd`). Use before `gh pr create` when the change must be proven to work, not just tested in passing. Verifies only — it does not fix; on FAIL it reports the gaps and stops.

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。

Turn a vague idea into a concrete, actionable spec through a short Socratic dialogue, then hand the result off to an existing moflo surface — a /flo ticket, a spell, or memory. Use BEFORE you have a defined unit of work, when the goal is still fuzzy.

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。

Quicken the codebase — an ad-hoc performance audit that reveals what drags (N+1 queries, needless re-renders, missing caching, memory leaks, redundant computation, hot-path complexity) and prescribes the fix, surfaced inline in the session. Scoped to your current diff by default, or to a path/area you name. Use before a perf-sensitive merge, when something feels slow, or when you want a targeted optimization pass. Replaces the old always-on `optimize` daemon worker (alias: /perf-audit).

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。

Adaptive learning for moflo agents via ReasoningBank: trajectory storage, verdict judgment, memory distillation, consolidation, and MMR retrieval. Use when building agents that should improve from experience across runs.

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。

Consult the Eldar — audit a project's moflo + Claude Code setup for portable, high-leverage gaps and guide remediation. Default mode is read-only audit with severity-ranked findings; --fix presents an interactive triage menu and walks the user through each chosen fix (healer, missing CLAUDE.md, sparse guidance, hook/MCP wiring, empty memory namespaces, stack→guidance gaps). Use when starting in a new project, when Claude feels lost or inefficient, when guidance/CLAUDE.md is sparse, or as a periodic health 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。

MUST USE for any real runtime debugging across ANY language or binary — crashes, silent failures, wrong responses, stuck processes, memory leaks, async misbehavior, unexplained timing, reverse engineering. Runs a hypothesis-driven loop: form ≥3 hypotheses, investigate in parallel, after 2 failed rounds spawn Oracles from orthogonal angles, confirm root cause, lock with a failing test, fix minimally, QA by actually USING the system, scrub artifacts. The actual HOW lives in `references/` — READ THEM. Triggers: 'debug this', 'why is X not working', 'hanging', 'attach a debugger', 'reverse engineer', 'pwndbg', 'gdb', 'lldb', 'node inspect', 'tsx debug', 'pdb', 'dlv', 'delve', 'rust-gdb', 'set a breakpoint', 'context window exploded', 'why is the response empty', 'attach the debugger', 'debug it', 'why is this happening', 'trace this bug', 'reproduce and fix', 'silent failure', 'HTTP 200 but empty', 'why did it stop', 'inspect the binary', 'reverse engineering', 'playwright'.

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 Mastra framework guide. Teaches how to find current documentation, verify API signatures, and build agents and workflows. Covers documentation lookup strategies (embedded docs, remote docs), core concepts (agents vs workflows, tools, memory, RAG), TypeScript requirements, and common patterns. Use this skill for all Mastra development to ensure you're using current APIs from the installed version or latest documentation.

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。

Learn project rules from accumulated fix patches in .unikit/code/patches/. Analyzes past mistakes, extracts prevention points, classifies each as a coding rule (→ RULES.md) or a per-skill workflow rule (→ skill-context), and proposes them for approval. Use when the user wants to turn past fixes into rules — "evolve", "evolve the rules", "learn from past mistakes", "learn from the fixes", "analyze the patches", "what rules should we add from recent fixes". Best run after several /unikit-fix sessions have left patches behind. This derives rules from accumulated patch history — to add a single rule by hand use /unikit-rules, and to promote mature RULES.md entries into the knowledge base use /unikit-memory migrate-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。

Pre-compaction housekeeping. Walks a checklist (persistent memory updates, git hygiene, trash cleanup) plus an open-judgment audit, produces an SBAR with a go/no-go recommendation, and emits a copy-pasteable resume prompt for the post-compaction agent if work remains. Run this immediately before /compact.

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 persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases.

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 AI-first interfaces that build ongoing relationships through memory, trust evolution, and collaborative planning, not just isolated screen interactions

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。

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature.

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Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project tracking.

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Design, build, deploy, and operate production AI agent systems — single agents, multi-agent teams, and autonomous swarms. Complete methodology from agent architecture through orchestration, memory systems, safety guardrails, and operational excellence.

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Implements the Active Learning Protocol (R3). Allows the agent to programmatically internalize lessons into MEMORY.md and generate structured "ask for help" requests.

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。

Error pattern tracking for AI agents. Detects corrections, escalates recurring mistakes, learns mitigations. The 'something's off' detector from the AI Brain series.

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Design, configure, debug, and optimize OpenClaw AI agent deployments. Master guide for gateway configuration, openclaw.json settings, model routing and fallback chains, skills development and publishing, cron job scheduling, memory systems (Qdrant, Neo4j, SQLite), Docker infrastructure, and Tailscale VPN networking. Includes config analyzer that audits your openclaw.json and suggests improvements, plus health checker that validates all OpenClaw subsystems. Built for AI agents — Python stdlib only, no dependencies. Use for OpenClaw setup, gateway debugging, skill building, cron management, model optimization, cost reduction, and infrastructure troubleshooting.

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Structured memory system for OpenClaw agents. Context death resilience (checkpoint/recover), structured storage, Obsidian-compatible markdown, local semantic search, and session transcript repair.

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Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.

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。

Agente de contexto para continuidade entre sessoes. Salva resumos, decisoes, tarefas pendentes e carrega briefing automatico na sessao seguinte.

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。