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cccskills

「feedbacks」の検索結果

6 件 ・ 関連度順

概要と使いどころ

Expert-level climatology covering climate system components, climate forcing and feedbacks, paleoclimatology, climate modeling, and climate change science.

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。

luokai0/ai-agent-skills-by-luo-kai122026年5月6日 更新

libafl

無料

Builds custom fuzzers with LibAFL, the modular Rust fuzzing library. Covers composing observers, feedbacks, mutators, schedulers, and executors into a fuzzer for targets the standard tools do not fit. Use when writing a bespoke fuzzer or mutator, fuzzing a non-standard target or architecture, implementing a fuzzing research idea, or when libFuzzer and AFL++ lack the control you need.

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。

trailofbits/skills7,5042026年10月10日 更新

Use when targeting Global Biogeochemical Cycles (GBC) or deciding whether a biogeochemical-cycling manuscript fits this venue. Encodes the journal's system-scale fluxes/budgets/feedbacks fit, the quantitative biogeochemistry evidence bar, AGU FAIR data-deposition expectations, house style, official-submission re-check, and desk-reject heuristics.

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。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when framing the system and describing the model for an Agricultural Systems (AgSy) manuscript — defining system boundaries, components, hierarchical levels, and feedbacks, then choosing, describing, and calibrating the model (process/simulation, whole-farm, bio-economic, agent-based, or integrated assessment). This is the distinctive core of an AgSy paper. Structures the model and system; it does not run code for you.

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。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Think and work like an expert Landscape Ecologist. Use when a task calls for Landscape Ecologist judgment. Reasons from scale-explicit pattern-process feedbacks, configuration over composition, and functional rather than graphical connectivity through FRAGSTATS, landscapemetrics, Circuitscape resistance surfaces, NLMR neutral models, and block cross-validation, while treating MAUP grain artifacts, classification error propagation, isolation-by-distance confounding isolation-by-resistance, and resistance surfaces unvalidated by movement as first-class failure modes.

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。

K-Dense-AI/scientific-agents2002026年10月3日 更新

Climate physics, forcing, feedback, and attribution. Covers the greenhouse effect and radiative forcing, climate sensitivity, fast and slow feedbacks, the paleoclimate record, detection and attribution science, regional climate impacts, and the distinction between weather, climate variability, and forced climate change. Use when reasoning about global warming mechanisms, interpreting temperature or CO2 records, evaluating attribution claims for extreme events, or distinguishing signal from natural variability.

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。

Tibsfox/gsd-skill-creator712026年7月20日 更新