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cccskills

「system」の検索結果

7,921 件 ・ 関連度順

概要と使いどころ

Cultivate defensive situational awareness, threat assessment, and mental clarity under pressure. Covers the Cooper color code awareness system, body language reading and intent detection, verbal de-escalation, moving mindfulness in public spaces, combat focus and the OODA loop, rapid grounding techniques for acute stress, context-specific integration, and ongoing review and refinement of awareness skills. Use when entering unfamiliar or potentially hostile environments, needing to assess a situation for safety, de-escalating a verbal confrontation, or integrating awareness practice into daily movement.

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。

pjt222/agent-almanac422026年10月15日 更新

metal

無料

Extract the conceptual essence of a repository as skills, agents, and teams — the project's roles, procedures, and coordination patterns expressed as agentskills.io-standard definitions. Reads an arbitrary codebase and produces generalized definitions that capture WHAT the project does and WHO operates it, without replicating HOW it does it. Use when onboarding to a new codebase and wanting to understand its conceptual architecture, when bootstrapping an agentic system from an existing project, when studying a project's organizational DNA for cross-pollination, or when creating a skill/agent/team library inspired by a reference implementation.

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。

pjt222/agent-almanac422026年10月15日 更新

Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.

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。

pjt222/agent-almanac422026年10月15日 更新

Implement pharmaceutical serialisation and track-and-trace systems compliant with EU FMD, US DSCSA, and other global regulations. Covers unique identifier generation, aggregation hierarchy, EPCIS data exchange, and verification endpoint integration. Use when implementing serialisation for a new product launch, integrating with the EMVS/NMVS, designing DSCSA-compliant transaction exchange, building an EPCIS event repository, or extending serialisation to additional markets (China, Brazil, Russia).

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。

pjt222/agent-almanac422026年10月15日 更新

Systematically interpret mass spectra to determine molecular formula, identify fragmentation pathways, and propose molecular structures. Covers ionization method assessment, molecular ion identification, isotope pattern analysis, common fragmentation losses, and purity evaluation.

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。

pjt222/agent-almanac422026年10月15日 更新

Systematically interpret nuclear magnetic resonance spectra (1H, 13C, DEPT, and 2D experiments) to elucidate molecular structure. Covers chemical shift assignment, coupling pattern analysis, integration, and correlation of multi-dimensional data into coherent structural proposals.

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。

pjt222/agent-almanac422026年10月15日 更新

learn

無料

AI systematic knowledge acquisition from unfamiliar territory — deliberate model-building with feedback loops. Maps spaced repetition principles to AI reasoning: survey the territory, hypothesize structure, explore with probes, integrate findings, verify understanding, and consolidate for future retrieval. Use when encountering an unfamiliar codebase or domain, when a user asks about a topic requiring genuine investigation rather than recall, when multiple conflicting sources require building a coherent model, or when preparing to teach a topic and deep understanding is required first.

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。

pjt222/agent-almanac422026年10月15日 更新

Set up systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Use when starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.

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。

pjt222/agent-almanac422026年10月15日 更新

Systematically interpret infrared spectra to identify functional groups present in a sample. Covers diagnostic region analysis (4000-1500 cm-1), fingerprint region assessment (1500-400 cm-1), hydrogen bonding effects, and compilation of a functional group inventory with confidence levels.

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。

pjt222/agent-almanac422026年10月15日 更新

Implement electronic signatures compliant with 21 CFR Part 11 Subpart C and EU Annex 11. Covers signature manifestation (signer, date/time, meaning), signature-to-record binding, biometric vs non-biometric controls, policy creation, and user certification requirements. Use when a computerized system requires legally binding electronic signatures for GxP records, when replacing wet-ink signatures in regulated workflows, when implementing batch release or document approval workflows, or when a regulatory gap reveals missing signature controls.

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。

pjt222/agent-almanac422026年10月15日 更新

Systematically interpret Raman spectra to identify molecular vibrations, assess polarizability-driven selection rules, compare with complementary IR data, and evaluate depolarization ratios for symmetry assignment. Covers Raman-active mode identification, fluorescence interference mitigation, and reference spectrum matching.

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。

pjt222/agent-almanac422026年10月15日 更新

Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.

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。

pjt222/agent-almanac422026年10月15日 更新

Develop a gas chromatography method from scratch: define analytical objectives, select column chemistry, optimize temperature programming, choose carrier gas and detector, and validate initial system performance for target analytes in a given matrix.

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。

pjt222/agent-almanac422026年10月15日 更新

Build and maintain a library collection through acquisitions, weeding (deaccessioning), collection assessment, reader advisory, and interlibrary loan coordination. Covers selection criteria, collection development policies, the CREW/MUSTIE method for weeding, usage analysis, and responsive collection management. Use when building a new collection with a defined scope and budget, assessing an existing collection for gaps or outdated materials, when shelves are overcrowded and systematic weeding is needed, or when establishing a formal collection development policy.

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。

pjt222/agent-almanac422026年10月15日 更新

Design serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.

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。

pjt222/agent-almanac422026年10月15日 更新

Establish Service Level Objectives (SLO), Service Level Indicators (SLI), and Service Level Agreements (SLA) with error budget tracking, burn rate alerts, and automated reporting using Prometheus and tools like Sloth or Pyrra. Use when defining reliability targets for customer-facing services, balancing feature velocity against system reliability through error budgets, migrating from arbitrary uptime goals to data-driven metrics, or implementing Site Reliability Engineering practices.

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。

pjt222/agent-almanac422026年10月15日 更新

Implement layered collective defense using alarm signaling, role mobilization, and proportional response. Covers threat detection, alert propagation, immune response patterns, escalation tiers, and post-incident recovery for distributed systems and organizations. Use when designing defense-in-depth where no single guardian covers all threats, building incident response that scales with severity, or when current defense is over-reactive to every alert or under-reactive to genuine threats.

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。

pjt222/agent-almanac422026年10月15日 更新

Design an acoustic levitation system that uses standing waves to trap and suspend small objects at pressure nodes. Covers ultrasonic transducer selection, standing wave formation between a transducer and reflector, node spacing and trapping position calculation, acoustic radiation pressure analysis, and phased array configurations for multi-axis manipulation. Use when designing contactless sample handling for chemistry, biology, materials science, or demonstration purposes.

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。

pjt222/agent-almanac422026年10月15日 更新

Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.

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。

pjt222/agent-almanac422026年10月15日 更新

Create interactive maps, elevation profiles, and spatial visualizations from GPX tracks, waypoints, or route data using R (sf, leaflet, tmap) or Observable (D3, deck.gl). Covers data import, coordinate system handling, map styling, and export to HTML or image formats. Use when visualizing a planned or completed tour route on an interactive map, creating elevation profiles for hiking or cycling routes, overlaying waypoints and POIs on a basemap, or building a web-based trip dashboard.

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。

pjt222/agent-almanac422026年10月15日 更新

Unify metrics, logs, and traces for cohesive debugging. Implement exemplars for log-to-trace linking, build unified dashboards using RED/USE methods, and enable rapid root cause analysis across observability signals. Use when investigating complex incidents spanning multiple systems, reducing mean time to resolution, implementing distributed tracing, or moving from siloed tools to a unified observability platform.

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。

pjt222/agent-almanac422026年10月15日 更新

Add an external probabilistic classifier to a decision path without letting it take the path over. Covers finding the externally-graded rows you are already logging, measuring the confidence separation between the overrides it got right and the ones it got wrong, choosing an operating point your data licenses, failing open at the call site, and proving the whole arrangement offline with no API key. Applies to any service returning a score you can order — TypeSafe AI's System One models (Jev) are the worked example. Most of the method transfers to a local model or a second heuristic, with the parts that do not called out where they arise. Use when a rule-based path is wrong often enough to hurt, when someone proposes replacing a heuristic with a model, when a confidence threshold needs a defensible value, or when an oracle already in production has never been graded.

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。

pjt222/agent-almanac422026年10月15日 更新

Set up centralized log aggregation with Loki and Promtail (or ELK stack), including log parsing, label extraction, retention policies, and integration with metrics for correlation. Use when consolidating logs from multiple services into a searchable system, replacing local log files with centralized queryable storage, correlating logs with metrics and traces, implementing structured logging with label extraction, or troubleshooting production incidents requiring cross-service log analysis.

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。

pjt222/agent-almanac422026年10月15日 更新

Create a Dockerfile for R projects using rocker base images. Covers system dependency installation, R package installation, renv integration, and optimized layer ordering for fast rebuilds. Use when containerizing an R application or analysis, creating reproducible R environments, deploying R-based services (Shiny, Plumber, MCP server), or setting up consistent development environments across machines.

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。

pjt222/agent-almanac422026年10月15日 更新