225 skills · sorted by relevance
Audit website images for excessive file sizes, unnecessary metadata, and loading issues. Apply format-specific compression and cleanup, then compare file sizes before and after changes to show the resulting reduction in image data.
Audit PDFs linked from a website by checking file sizes and response headers, the information returned by the server. Flag large downloads for compression review and suggest HTML versions when the content needs a searchable web page.
Audit website image file sizes against guidelines for photos, graphics, hero images, and thumbnails. Report oversized assets with target sizes, distinguish compression issues from dimension mismatches, and guide format conversion and delivery of smaller variants to mobile devices.
Configure images and videos as surface textures in Three.js, and use environment images for backgrounds and reflections. Adjust placement, color settings, repetition, and filtering, then manage texture memory through compression, reuse, and disposal.
Work with large scientific arrays by storing data in smaller blocks and reading selected portions. Guides compression, cloud storage, format migration, and NumPy, Dask, or Xarray integration while preserving dimensions, units, and other metadata.
Tune vector indexes, which organize data for similarity searches, to balance speed, memory use, and missed matches. Choose index types, adjust HNSW settings, and assess compression options using benchmarks with real queries.
Rewrite lengthy prompts, tool descriptions, and agent instructions into compact text while preserving requirements, exceptions, and exact values. The process records removed content and compares token counts, the units models use to measure text length.
Identify and unpack malware compressed with UPX to prepare executable files for static analysis, which examines code without running it. The workflow covers altered header repair, debugger-based recovery, and validation before analysis in Ghidra or IDA.
Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.
Train smaller language models using knowledge distillation, where a larger model supplies examples or prediction targets. This skill covers training losses, generated training data, parameter choices, and output comparisons to support model compression and specialized student models.
Reduce the size of AGENTS.md or RESOLVER.md by grouping skills into functional areas. Each area keeps a list of its sub-skills, helping the agent find the appropriate skill through a two-level routing structure.
Reduce image file sizes by compressing individual files or whole folders into WebP, PNG, or JPEG. The skill selects an available compression tool automatically and lets you control quality, include subfolders, and choose whether to keep originals.
Extends the core PUA skill with local performance notes, recovery of task state after conversation compression, KPI report cards, and style switching. It tracks established working habits across sessions and keeps feedback in a local file.
Learn how instructions, documents, tool results, and conversation history form an AI model’s context. Understand attention limits, why information in the middle can be missed, and how relevance and placement inform decisions about what to include.
Improve screenshots and digital images by increasing resolution, sharpening details, and reducing noise and compression artifacts. Prepare images for presentations, documentation, blogs, or social posts, with folder-wide batch processing and backups of the originals.
Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.
Display a session dashboard showing Claude token usage, API costs, estimated savings from TLDR file summaries, cache reuse, and hook activity. Use it to compare compression results and check whether TLDR and automated hooks are running.
Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.
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Generate compression reports for video processing. Use when you need to create structured JSON reports with duration statistics, compression ratios, and segment details after video processing.
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Token-efficient prompt compression techniques for cost optimization
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Lossless session context compression for token efficiency. Extracts entities, decisions, and state into compact format before context window fills. 10-30x reduction in context size while preserving all actionable information.
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Upload video/audio media to BytePlus VOD (Video on Demand) storage, returning the Vid and playback references; supports both local file upload (ApplyUploadInfo + TOS + CommitUploadInfo) and URL pull upload (UploadMediaByUrl); also supports AI-based comprehensive quality restoration on already-uploaded videos (removing compression artifacts, noise, scratches, improving clarity, and configuring Pro-tier color depth). Trigger keywords: upload video, upload media, upload to VOD, URL upload, pull upload, local upload, file upload, UploadMediaByUrl, ApplyUploadInfo, media ingestion, quality restoration, quality enhancement, comprehensive restoration, video denoising, denoise, compression artifact removal, color depth, bit depth, 10-bit, 12-bit, 16-bit.
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Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
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Schmidhuber's compression progress as intrinsic curiosity reward for learning systems.
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