「recall」の検索結果

Recall past conversations, preferences, project history, and decisions from ReMe in Claude Code, or check the ReMe 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。

Recall past facts, preferences, and decisions from ReMe; inspect memory delivery, service health, and the consolidation schedule; or consolidate memory when requested.

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Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall / FAR / PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include "RCA on my ChangeNet model", "why is my AOI model failing", "audit ChangeNet predictions", "investigate FAR regressions", "root cause analysis on visual-changenet".

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Run TAO Data Services KPI analysis for object detection, comparing inference annotations against ground truth to compute per-class TP/FP/FN/TN, precision, recall, accuracy, and AP at a fixed IoU. Use when an object detection workflow needs per-class mAP reported after inference, or when the user asks to "run KPI analyze", "compute detection mAP", or "score my OD predictions against ground truth".

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Ingest OpenClaw session/history data into Obsidian as distilled knowledge. Use for importing or mining ~/.openclaw sessions, memory files, or logs; use wiki-agent for targeted topic-only cross-agent recall.

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Ingest Pi coding-agent session history into Obsidian as distilled knowledge. Use for importing or mining ~/.pi/agent/sessions or Pi conversation logs; use wiki-agent for targeted topic-only recall.

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Ingest Hermes agent history into Obsidian as distilled knowledge. Use for importing or mining ~/.hermes sessions, memories, or logs; use wiki-agent for targeted topic-only cross-agent recall.

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Search one AI agent's raw history for a specific topic, ingest only matching sessions, and return a synthesized answer. Use for targeted cross-agent recall; use wiki-history-ingest for bulk archival ingestion.

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Ingest Codex CLI conversation/session history into Obsidian as distilled knowledge. Use for importing or mining ~/.codex sessions, rollout files, indexes, or transcript logs; use wiki-agent for targeted topic-only recall.

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[omh] Outdated or wrong Hermes memory entries: English-canonical Hermes memory-review guidance: inspect USER.md and MEMORY.md claims and prepare a native write diff without invoking, applying, or observing a native write; for a new fact use memory-new, and for a past decision use decision-recall. Use when the user says: memory-sync, memory curation, memory review, memory inspect, memory check, memory update, context cleanup, curate memory.

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[omh] Project fact to remember across future sessions: capture one bounded durable project or product memory candidate through explicit remember, refuse, or defer review; for existing Hermes memory use omh-memory-sync, and for a past decision use decision-recall. Use when the user says: memory-new, new memory, project memory, product memory, remember this project, remember this product, do not save, do not save this token.

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Query, search, and download data from the openFDA API for drugs, devices, foods, tobacco, cosmetics, animal and veterinary products, substances, and transparency data. Use for FDA adverse events, recalls, labeling, approvals, shortages, 510(k) clearances, NDC lookups, and any FDA safety or regulatory data query across all 28 API endpoints.

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Requests about what is remembered, however short, from an ask to remember one thing to asks to say what is on file, correct it, or forget it; a bare save or an answer from the profile alone does not cover these. Also applying what the store already knows, recalling an older fact (a past recipient, a size, a recurring order) when it would change the pick, and deciding what should outlast the session. Not needed when the request carries no personal context to apply or keep.

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Start, structure, and grow a persistent research wiki indexed in pro-workflow's SQLite knowledge base. Each wiki is a folder of markdown pages with provenance, plus a shadow FTS5 index so any session can recall it. Use when the user says "start a wiki", "add to wiki", "compile a page", "wiki on X", or wants a long-lived knowledge base on a topic, paper, product, person, project, or codebase.

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Surface past learnings relevant to the current task before starting work. Searches correction history, recalls past mistakes, and applies prior patterns. Use when starting a task, saying "what do I know about", "previous mistakes", "lessons learned", or "remind me about".

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Apply the Serial Position Effect — first and last items in a sequence are recalled best. Use when ordering menus, lists, and steps. For emphasising one item regardless of its position, use `von-restorff-effect` (ui-design).

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Search relevant memories by keyword and optional type filter. When you need to recall past information, find user preferences, or check learned patterns.

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Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

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Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

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Qualify a check before it is allowed to clear anything — prove it can detect the failure it is meant to catch. Seeds known defects into a copy of a real artifact plus a clean control, runs the checker, and reports recall and false-positive rate into a qualification ledger. Use when the user says "does this check work", "qualify the gate", "test my reviewer", "seed defects", "vaccinate", "qualify the checks", "can I trust this review", "does it pass for the right reason", or before relying on any automated check or referee simulation for a decision that matters. NOT a code fixer and NOT a reviewer itself — it grades the grader.

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把当前诊断的关键状态存到本地，下次回来可以接着用。 触发方式：/dbs-save、/存档、「保存这次诊断」「记下来」「这个结论留着」 Save the current diagnosis state to disk for cross-session recall. Trigger: /dbs-save, "save this diagnosis", "remember this"

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Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.

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。

Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.

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。

Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitor_only, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.

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。