Results for “base-files”

Manages a multi-layered knowledge system for ingesting, organizing, syncing, and retrieving knowledge across local files, MCP memory, vector stores, and Git repos.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Reconciles project documentation, agent memory, and rule files against the actual codebase after a development session, ensuring accuracy and consistency across all knowledge layers.

Read and write genomic alignment and variant files in Python with pysam (htslib bindings) — SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences, plus region extraction and per-base coverage/pileup. Use when scripting NGS data-processing pipelines that parse, filter, index, or compute coverage over BAM/CRAM/VCF files. Part of the AlterLab Academic Skills suite.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Generates Playwright-based browser end-to-end tests for Adobe App Builder SPAs and AEM extensions, including configs, test files, and CI workflows.

Analyze Zeek dns.log files to detect DNS-based data exfiltration by computing Shannon entropy, flagging long subdomain labels, and identifying anomalous query patterns.

Manages a long-term Markdown knowledge base for software projects, capturing architecture, decisions, and conventions in `.lore/` with commands to init, sync, query, audit, compress, and mirror to platform files.

Suggests relevant GitHub Copilot instruction files from the awesome-copilot repository based on current repository context and chat history, avoiding duplicates with existing instructions and identifying outdated ones.

Parse Windows PowerShell Script Block Logs (Event ID 4104) from EVTX files to detect obfuscated commands, encoded payloads, and living-off-the-land techniques.

Handle files and binary data in n8n workflows correctly, covering the $binary vs $json split, reading/writing binary, preserving binary across transforms, and the agent-tool binary boundary.

Tighten annotations in existing Python code with a focused, low-churn workflow based on Honnibal's tighten-types skill. Use to improve changed files, remove avoidable Any, make container and return types precise, or reduce type-checker errors without broad refactoring.

Generate tests from Allium specifications. Use when the user wants to propagate tests, generate test files from a spec, write tests for a specification, create property-based tests, produce state machine tests, check test coverage against spec obligations, or understand what tests a specification requires.

Automatically organizes files in a directory into a clean structure based on configurable rules — by file type, date, project, or priority — with support for duplicate detection, naming conventions, and archival strategies. Use when the user requests file organization or provides relevant inputs for this workflow.

Baseline the EFI System Partition and hunt malicious EFI binaries (ESPecter, BlackLotus, Bootkitty, Glupteba) by mounting the ESP, hashing and verifying boot loaders, scanning with YARA, and detecting anomalous non-EFI files.

Use when tasks involve Xget URL rewriting, registry/package/container/API acceleration, integrating Xget into Git, download tools, package managers, container builds, AI SDKs, CI/CD, deployment, self-hosting, or adapting commands and config from the live README `Use Cases` section into files, environments, shells, or base URLs.

Crawl entire websites using Cloudflare Browser Rendering /crawl API. Initiates async crawl jobs, polls for completion, and saves results as markdown files. Useful for ingesting documentation sites, knowledge bases, or any web content into your project context. Requires CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN environment variables.

Use when tasks involve Xget URL rewriting, registry/package/container/API acceleration, integrating Xget into Git, download tools, package managers, container builds, AI SDKs, CI/CD, deployment, self-hosting, or adapting commands and config from the live README `Use Cases` section into files, environments, shells, or base URLs.

Detects fileless malware and in-memory attacks that execute entirely in RAM without writing persistent files to disk, evading traditional antivirus. Provides detection rules for PowerShell-based attacks, reflective DLL injection, WMI persistence, and registry-resident malware.

Autonomous spec-driven development agent. Syncs DEV_SPEC.md into chapter-based reference files, identifies the next pending task from the schedule, implements code following spec architecture and patterns, runs tests with up to 3 auto-fix rounds, and persists progress with atomic commits. Use when user says "auto code", "自动开发", "自动写代码", "auto dev", "一键开发", "autopilot", or wants fully automated spec-to-code workflow.

Autonomous spec-driven development agent. Syncs DEV_SPEC.md into chapter-based reference files, identifies the next pending task from the schedule, implements code following spec architecture and patterns, runs tests with up to 3 auto-fix rounds, and persists progress with atomic commits. Use when user says "auto code", "自动开发", "自动写代码", "auto dev", "一键开发", "autopilot", or wants fully automated spec-to-code workflow.

Autonomous spec-driven development agent. Syncs DEV_SPEC.md into chapter-based reference files, identifies the next pending task from the schedule, implements code following spec architecture and patterns, runs tests with up to 3 auto-fix rounds, and persists progress with atomic commits. Use when user says "auto code", "自动开发", "自动写代码", "auto dev", "一键开发", "autopilot", or wants fully automated spec-to-code workflow.

Build and maintain a persistent markdown wiki that an LLM updates on the user's behalf, usually inside an Obsidian vault or git-tracked notes repo. Use when raw sources such as web articles, papers, meeting notes, transcripts, screenshots, or past analyses need to be turned into an interlinked knowledge base with immutable source files, LLM-written wiki pages, `index.md`, `log.md`, schema rules in `AGENTS.md` or `CLAUDE.md`, source summaries, query notes, and recurring lint passes. Triggers on: llm-wiki, personal wiki, obsidian wiki, research vault, knowledge base, source ingest, persistent notes, wiki maintenance, source summaries, query filing.

Transform diverse inputs (YouTube videos/audio, existing Suno songs, raw lyrics, audio files, or conversational ideas) into highly optimized Suno V5 custom song generation prompts with intelligent character optimization (5000 lyrics, 1000 style limits), proven metatag reliability, V5-enhanced emotion tags, and template-based best practices. Use when user wants to create music with Suno, provides content for song generation, or needs help crafting effective V5 prompts.

Access NCBI GEO (Gene Expression Omnibus) for gene expression and functional genomics data — search and download microarray and RNA-seq datasets by GSE, GSM, GPL, or GDS accession and retrieve SOFT, MINiML, and series matrix files. Use when locating public expression datasets, fetching processed expression matrices, downloading a study's supplementary files, or sourcing per-study transcriptomics data for differential-expression analysis. For raw FASTQ sequencing reads by SRA/ENA run accession use alterlab-ena; for reference tissue-expression baselines (median TPM across human tissues) use alterlab-gtex; for cancer cohort somatic mutations and copy-number use alterlab-cbioportal. Part of the AlterLab Academic Skills suite.

Persistent cross-session memory for AI agents. Mem0 stores user preferences, past decisions, domain knowledge, and agent learnings across all sessions, all tools, and all users. Complements planning-with-files (task-level memory) with long-term agent intelligence (CRM + personal knowledge base layer). Use when asked to "remember this", "store preference", "mem0", "long-term memory", "user memory", "agent memory", or when building multi-session agents that need to recall past interactions.

Access the AlphaFold DB of 200M+ AI-PREDICTED protein structures — retrieve models by UniProt accession, download PDB/mmCIF files, and analyze prediction confidence metrics (pLDDT, PAE). Use when a UniProt ID needs a computationally predicted 3D structure or when no experimental structure exists, for homology modeling, protein engineering, or structure-based drug discovery; for EXPERIMENTALLY determined structures (X-ray, cryo-EM, NMR) prefer alterlab-pdb, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.

This skill should be used when a SKILL.md file needs compression, deduplication, or token reduction. It provides an embedding-based compression pipeline that detects and removes redundant chunks within SKILL.md files using local embeddings (all-MiniLM-L6-v2). Two-pass approach: (1) free intra-skill deduplication via cosine similarity clustering, (2) optional LLM-judged graded eval to detect pretraining overlap. Typical result: 25-46% token reduction with zero quality loss. This skill is not intended for editing skill content, creating new skills, routing optimization, or cross-skill deduplication.

Maestro — declarative E2E mobile UI testing framework by mobile.dev. YAML-based flow files, single tool for Android + iOS (and Compose Multiplatform / Flutter / React Native). Built-in cloud runner, recording mode, JS scripting for complex assertions, screen state diffing, no flakiness from explicit waits. USE WHEN: user mentions "Maestro", "maestro test", "mobile E2E", "cross-platform UI test", "maestro studio", "mobile.dev cloud", ".maestro" folder, "launchApp" YAML DO NOT USE FOR: web E2E - use `testing/playwright` DO NOT USE FOR: unit tests - use `testing/kotest`, `testing/vitest`, etc. DO NOT USE FOR: instrumented Android tests - use Espresso/Compose Test DO NOT USE FOR: snapshot tests - use `testing/compose-snapshot`

Set up pre-commit hooks on a Python project — standard file-hygiene checks plus a security gate (gitleaks secret scanning, Trivy filesystem scan for CVEs/secrets/misconfigs, and Bandit Python SAST). Use this whenever the user wants to add, configure, or fix pre-commit hooks on a Python repo, mentions .pre-commit-config.yaml, wants secret/vulnerability/SAST scanning on commits, or is setting up code-quality guardrails — even if they just say 'add pre-commit hooks' without naming the tools. Especially for uv-based GenAI/LLM backends. Handles the setup gotchas that break first-time installs: the Trivy binary, the required data/html.tpl report template, bandit[toml] + [tool.bandit] config, and the right .gitignore entries.

Configure, deploy, and manage Senpi Trading Runtime (OpenClaw plugin @senpi-ai/runtime) for automated on-chain position tracking with DSL trailing stop-loss protection. Use when a user needs to create or modify runtime YAML files, configure DSL (Dynamic Stop-Loss) exit engine parameters (phases, tiers, time-based cuts), set up the position_tracker scanner to monitor a wallet's positions on Hyperliquid, install/list/delete runtimes via CLI, or inspect DSL-tracked positions. The runtime does NOT create strategy wallets; create/get the strategy wallet via Senpi MCP first, then link that existing wallet in runtime YAML. Triggers on mentions of senpi, Senpi runtime, DSL exit, stop-loss tiers, position tracker, trailing stop, openclaw senpi, dsl_preset, or strategy YAML configuration."