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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases.
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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
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Establish MSBuild/.NET build performance baselines before optimizing. USE FOR: unranked slow builds; cold, warm, incremental or no-op measurements; before/after comparisons; CI output caching; static graph, artifacts output and dependency graph trimming. Start here before build-perf-diagnostics, incremental-build or build-parallelism. DO NOT USE FOR: known repeated-restore fixes (use eliminate-redundant-restores), non-MSBuild builds. For measured target/task/analyzer bottlenecks, use build-perf-diagnostics.
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Analyze an MSBuild solution, solution filter, or Build.proj that schedules multiple project files. USE FOR: measured idle worker nodes, `-m` that does not scale, a serial ProjectReference critical path, graph builds, or an MSBuild task that builds a Projects list. Requires at least two distinct project files whose scheduling or throughput must be analyzed. DO NOT USE for non-MSBuild build systems.
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Canonical patterns for writing custom MSBuild targets. USE FOR: diagnosing and fixing custom target authoring anti-patterns; broken SDK target chains across files (e.g., Directory.Build.targets silently redefining SDK targets); targets that replace CompileDependsOn instead of extending it with $(CompileDependsOn); query targets returning stale results from Outputs vs Returns misuse; missing Inputs/Outputs causing unnecessary rebuilds; missing FileWrites registration. Covers DependsOnTargets vs BeforeTargets vs AfterTargets, the Build→CoreBuild three-level pattern, and the $(XxxDependsOn) chain-extension pattern. DO NOT USE FOR: incremental build tuning (use incremental-build), parallelization (use build-parallelism), general anti-patterns (use msbuild-antipatterns), non-MSBuild build systems.
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Decision SOP for serving LLMs with vLLM. Covers PagedAttention mental model, quantization/parallelism/batching tradeoffs, OOM triage, and when NOT to use vLLM. Activates when a coder-agent is choosing or tuning an inference engine, debugging vLLM throughput/latency/OOM, or comparing vLLM against TGI/SGLang/TensorRT-LLM/llama.cpp.
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Python-based workflow manager for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake resolves execution order and parallelism. Runs local, SLURM, LSF, AWS, GCP via profiles; per-rule conda/Singularity envs. For NGS pipelines, ML training, and multi-step file processing. Use Nextflow for Groovy dataflow or nf-core integration.
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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
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Use DeepSpeed for distributed training, inference acceleration, ZeRO configuration, parallelism/MoE design, profiling, autotuning, and operational diagnostics.
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Use ColossalAI for distributed PyTorch training, large-model parallelism, Booster plugins, ShardFormer, Colossal-Inference, and first-party application workflow routing.
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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
中文简介正在准备中。当前显示原文说明。
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases.
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Remove signs of AI-generated writing from prose, and keep it out of a repo. Use when drafting, editing, or reviewing text to make it sound natural and human, or when auditing a whole docs folder for AI slop. Detects 41 patterns across content, language, style, communication, filler, and rhetoric, including: significance inflation, promotional language, -ing tails, vague attributions, AI vocabulary, copula avoidance, negative parallelisms, false agency, em dash overuse, chatbot artifacts, hedging stacks, staccato drama, and aphorism formulas. Includes voice calibration, a no-fabrication rule, statistical tells, and a draft -> audit -> final rewrite loop.
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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
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Design CI/CD pipelines that run test suites. Covers GitHub Actions and GitLab CI templates, parallelism and sharding, artifact management, flaky-test quarantine, test-result publishing, coverage quality gates, OIDC keyless deploy, and copy-paste workflows for Playwright, Jest, and multi-stage pipelines. Use when: "CI/CD," "GitHub Actions," "pipeline," "test in CI," "GitLab CI," "continuous integration," "test automation pipeline," "shard tests in CI." Not for: per-test flaky healing at runtime — use test-reliability; go/no-go release decisions and smoke-test checklists — use release-readiness; test-result dashboards and trend reporting — use qa-metrics. Related: playwright-automation, qa-metrics, test-reliability, coverage-analysis, release-readiness.
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Use when training models across multiple GPUs or nodes, handling large models that don't fit in memory, or optimizing training throughput - covers DDP, FSDP, DeepSpeed ZeRO, model/data parallelism, and gradient checkpointingUse when ", " mentioned.
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De-slop pass for any text. Scans for the statistical fingerprints of AI writing (negative parallelism / "not X but Y", em-dash density, rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged both-sidesing), rewrites by meaning, then re-scans until the text is clean and sits in the right register for its genre: academic article, tweet, reddit post, email, blog, docs, marketing. Use when the user says "deslopify", "deslop", "de-slop this", "remove the AI tells", "humanize this", "make this not sound like AI", or invokes /deslopify. Also use before publishing any agent-drafted prose.
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Analyzes large tasks for independent subtasks that can be safely parallelized. Produces a DAG-based dispatch plan with dependency ordering and maximum parallelism. Use when a task has 5+ subtasks touching independent files, or the user asks to speed up or parallelize the work.
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Guides multi-GPU pre-training: DDP, FSDP2, ZeRO, tensor/pipeline/expert parallelism, fp8/Muon. Use when scaling a run, training MoE, or reproducing GPT-2 on rented GPUs.
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Applying motion study and time analysis to household work. Based directly on Lillian Gilbreth's *The Home-Maker and Her Job* and *Cheaper by the Dozen*, this skill covers the therblig-motion catalog, task decomposition, batch processing, parallelism, routine chart design, and the ergonomics of work surfaces. Use when diagnosing why a household is running out of time, designing a weekly routine, teaching task sharing to children, or reducing the friction of any repeated household task.
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World Extractable Value via Aptos orderless transactions with Strong Parallelism Invariance (SPI) guarantees and GF(3) conservation.
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Write or rewrite prose so it sounds like a human wrote it — not a frontier model. Named in homage to Rabindranath Tagore, whose prose carried what frontier models reach for and miss: a point of view, specificity over abstraction, and restraint over puffery. Merges two complementary approaches: a 29-pattern catalog of AI tells (from humanizer) plus an 8-rule operating system with an 8-dimension scoring gate (extending stop-slop). Use when drafting, editing, or reviewing any prose: essays, posts, docs, reports, emails. Detects and removes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary, passive voice, negative parallelisms, filler phrases, inanimate-verb constructions, narrator-from-a-distance voice, and metronomic rhythm. Adds back the things AI writing usually lacks: point of view, stakes, specificity, restraint, varied rhythm, and trust in the reader.
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Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.
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Execute TDD task pairs autonomously with RED-GREEN-REFACTOR verification. Orchestrates wave-based execution with strategic parallelism, routing TDD tasks to tdd-executor agents and non-TDD tasks to standard task-executor. Use when user says "execute tdd tasks", "run tdd tasks", "start tdd execution", or wants to execute TDD-paired tasks from create-tdd-tasks.
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