「recall」の検索結果

Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.

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。

Reads, searches and exports local Claude Code and Kimi CLI history without resuming work: timelines, verbatim user input, keyword or fuzzy recall, file recovery from transcripts, and which session ran a given command in a known time window. Use when the user asks what was said, wants a session ID or original context, needs proof of what a session contained, or needs past-session runtime evidence about commands, tool calls and hook runs — query this index instead of raw-scanning transcript files. Not for Codex (use read-codex-history); with no platform or several named, start at local-conversation-history.

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 asked 质量追溯体系怎么建, 产品追溯方案, 批次追溯, 一物一码, 客户要求可追溯性, 召回演练, 正向追溯和反向追溯, or plan product and batch traceability in a Chinese factory. Produces a traceability plan in Chinese: the trace unit and coding rule, the data to capture at each process step (material lots, work orders, equipment, people, inspection results), forward and backward trace paths, record retention, a mock recall drill with a target time, gaps against the current system, and a phased rollout, with industry rules (food, medical devices, automotive) marked to confirm.

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。

Store and recall with Supermemory: add memories and documents, hybrid search, upload files, tune settings. Trigger phrases: supermemory, memory engine, remember this.

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。

按需召回已归档的上下文细节：支持按关键词、阶段、类型和决策维度检索， 从 L2 归档和 Phase Ledger 中恢复压缩丢失的信息。

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。

Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.

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。

Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.

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 Apple's own documentation or a Swift compiler diagnostic explained rather than recalled.

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 the user provides 1-50 concrete bad-case search queries for one Viking Search app and wants local deterministic fixes. This skill only verifies request-level fine-operation interventions against a read-only baseline scene and delivers a console-ready configuration sheet, validated payloads, and a replay script. It must not mutate scenes, apps, dictionaries, datasets, recall core parameters, or online defaults.

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。

Recommend runtime and V2 scene management: run recommendation requests, manage recommend scenes and rules, and verify the deployed recommendation path.

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。

Viking Search tuning for specified policy directions. Use this when the user provides specific queries, a type of query, or a business policy direction, and asks to boost, suppress, or fix a class of search results through request-parameter passthrough. You must only perform read-only baseline evaluation and request-level candidate testing; do not modify search scenes, app config, dictionaries, recommend scenes, or primary recall parameters.

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 user asks to "improve my mAP", "why is my model overfitting", "my training is diverging", "read my results.csv", "interpret my training curves", "my AP50 is good but AP50-95 is bad", "my recall is low", "how do I pick learning rate", "which augmentations should I use", "should I use a bigger model", "tune hyperparameters", or asks how to train YOLO26 for detection, instance or semantic segmentation, pose, OBB, classification, or depth.

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。

Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.

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 a Montessori three-period lesson for introducing concepts through concrete materials and naming. Use when teaching vocabulary, classifications, or concepts through hands-on materials.

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。

Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters".

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。

Create study sessions using recall questions, chunking, mnemonics and explicit review intervals. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.

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。

Search past reasoning for relevant decisions and approaches

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。

Store a learning, pattern, or decision in the memory system for future recall.

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。

Query the memory system for relevant learnings from past sessions using semantic search.

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。

Query and maintain the user's local LLM Wiki from any Codex or Claude Code conversation or project. Use when the user asks to search personal knowledge, recall prior research or decisions, answer from the wiki, create durable notes, ingest a source into the wiki, update topic/concept/entity/question pages, write back useful answers, or run wiki lint/reindex 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。

Evaluate, fix, answer, and reply to GitHub pull request review comments and conversation comments. Handles both change requests (fix or skip) and reviewer questions (explain using the rationale recalled from past Codex session history). Use when the user asks to "resolve PR comments", "fix review comments", "address PR feedback", "handle review comments", "address review feedback", "respond to PR comments", "answer review questions", or "address code review".

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。

Evaluate, fix, answer, and reply to GitHub pull request review comments and conversation comments. Handles both change requests (fix or skip) and reviewer questions (explain using the rationale recalled from past Claude Code transcripts). Use when the user asks to "resolve PR comments", "fix review comments", "address PR feedback", "handle review comments", "address review feedback", "respond to PR comments", "answer review questions", or "address code review".

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。

Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.

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 recalling prior techniques at recon/weaponize, or recording a confirmed finding at report — cross-engagement pattern memory ranked by impact

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。