Results for “file-read”

Read, write, and manipulate DICOM medical imaging files, including pixel data extraction, metadata editing, anonymization, format conversion, and compression handling.

Read, write, and modify DICOM medical imaging files, including pixel data extraction, metadata manipulation, anonymization, and format conversion.

Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.

Analyze proteomics and metabolomics mass spectrometry data with PyOpenMS: read/write MS file formats, process spectra, detect and quantify features, identify peptides and proteins, and run end-to-end LC-MS/MS pipelines using ready-to-run scripts.

Generate production-ready Dataverse SDK solutions with architecture recommendations, data models, and implementation code for common business use cases.

Generates RFQ-ready 2D DXF files and optional SVG previews from a validated JSON spec, ideal for sheet and plate parts like brackets and hole patterns.

Iteratively builds websites with Stitch by reading a baton file, generating pages via MCP tools, integrating them, and writing the next task.

Reviews code using pi CLI with read-only tools. Delegates the review to pi (dev/pi) with a structured review rubric, running in read-only mode to prevent accidental edits. By default reviews uncommitted working tree changes (git diff HEAD) with pi restricted to the read tool; explicit targets (--branch/--diff/@file/PR) or --explore widen pi to read,grep,find,ls. Use when the user asks to "review code with pi", "pi review", "have pi review", "let pi review", or invokes /pi:review.

Generates Python code to read CSV or Excel files, extract name and quantity columns, create a bar chart, and save it as a JPG image.

Guides AI agents on working with a deprecated, read-only repository of Claude Skills, covering skill structure, development workflows, and upload automation.

Use when you need to annotate BAM alignments with a two-character tag based on overlaps with BED, GFF, or VCF annotation files, such as labeling reads by feature class or interval source.

Creates a machine-readable phased JSON plan bundle from a spec or feature description, producing a compact master manifest plus per-phase step files for resumable execution.

Use when code is hard to read, has duplication, is too complex, has grown beyond 300 lines, or needs restructuring without changing behavior. For extracting functions, simplifying nested logic, removing dead code, improving naming, splitting large files, and applying clean code principles. Activate when user says "clean this up", "this is messy", "simplify", "extract", or "improve readability".

Creates a machine-readable phased JSON plan bundle from a spec or feature description, producing a compact master manifest plus per-phase step files for resumable execution.

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.

Builds a navigable knowledge graph from any folder of files, with community detection, an audit trail, and outputs including interactive HTML, GraphRAG-ready JSON, and a plain-language report.

Builds a local knowledge graph of a codebase using tree-sitter and graph algorithms, then answers architecture questions by querying the graph instead of reading many files, saving tokens.

Mechanically replace static dependency call sites with wrapper or built-in abstraction calls across a bounded scope, such as migrating DateTime.UtcNow to TimeProvider.GetUtcNow() or File.ReadAllText to IFileSystem.

Package skills into publishable plugin or bundle formats across ClawHub, Claude, Hermes, AgentSkill, AgentSkills.so, and GitHub. Use when: converting a skill into a release plugin, generating manifests, trimming runtime-only files, and assembling a breakout-ready upload artifact.

Initializes production-ready logging for a Next.js project using Pino and pino-roll, with file output, log rotation, and request context. Handles server-side and client-side environments, including Vercel/serverless setups.

Use before merging a pythinker-code release PR to preview the user-facing CLI changelog in Chinese. Reads the changelog that changesets pre-generated in the release PR, then reuses sync-changelog's strip / classify / translate logic to render a Chinese preview. Writes no files.

Launches four parallel read-only sub-agents to review a git diff or file scope for regressions, security risks, performance issues, and contract gaps, then aggregates findings into a prioritized fix path.

Build Simulink models that co-simulate with Eclipse SUMO traffic simulator. Use when creating SUMO-Simulink co-simulation, traffic simulation, TraCI connection, vehicle-in-the-loop testing, or ADAS scenario validation with SUMO. Covers Server/Client setup, Reader/Writer/Actor block configuration, random traffic generation, ego vehicle control, and SUMO file creation. Also use when the user mentions SumoInterfaceLibrary, .sumocfg files, or wants to connect Simulink to an external traffic simulator.

Simulator.Company reactions specialist — comments, events, approvals, ratings and other feedback attached under an actor. Use when the user wants to comment on an actor, reply in a thread, read/like/pin the discussion, mark comments read, or get reaction stats. Activate when the user says "comment on this actor", "leave a note", "reply to", "show the comments/discussion", "pin this comment", "mark as read", "прокоментуй актор", "залиш коментар", "відповісти на коментар", "закріпи коментар", "оставь комментарий", "ответь в треде", "закрепи комментарий". For attaching FILES to a reaction use `simulator-attachments`; for the actor's own fields use `simulator-actors`.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Capture an Architecture Decision Record — what was decided, why, what alternatives were considered, and when to revisit. Writes a numbered ADR file to docs/adr/ in the active repo, with consistent formatting that downstream skills (improve-codebase-architecture, refactor-plan) can read. Use right after deciding something non-obvious so the reasoning isn't lost.

Drafts SEC- or state-filed Form ADV Parts 1A/1B/2A/2B for investment adviser registration, producing IARD-ready responses, brochures, and supplements. Use when drafting or amending Form ADV, preparing RIA registration, building Part 2A brochures or Part 2B supplements, or compiling IARD filings. Trigger: "Form ADV", "RIA registration", "investment adviser brochure", "Part 1A", "Part 1B", "Part 2A", "Part 2B", "IARD", "SEC filing", "state registration".

Use when creating new Excalidraw diagrams or reading, modifying, or comparing existing .excalidraw or .excalidraw.json files. Also use when the user asks for hand-drawn style diagrams, whiteboard sketches, architecture diagrams in Excalidraw format, or wants to export Excalidraw to SVG/PNG. Use this skill even if the user doesn't say "Excalidraw" — for example when they ask for a system diagram, flowchart, or architecture sketch that should look hand-drawn or informal.

Recognize when you're in a runaway loop and stop yourself. Use when the same tool call has repeated 3× with the same args, when the same error class has fired 3× in a turn, when you've been told the same correction twice, when "let me try one more variation" is the third small variation, or when a refinement loop is orbiting rather than converging. The right move on a trip is to switch from action to inquiry — read events.jsonl, list assumptions, look for contradictions, ask the human, file a chainlink — not to retry harder.

Package and publish a skill to the skills.sh community registry, a public GitHub repo, or both. Load when the user asks to publish a skill, share a skill publicly, submit a skill to the registry, release a skill, or when universal-skill-creator offers publishing as a final step after creation. Also triggers on "push this skill to skills.sh", "make this skill public", "share this skill", or "contribute this skill to the community". Validates quality before publishing, packages correctly (zip for multi-file, .md for atomic), writes a README if missing, and publishes via npx skills CLI.

Drafts litigation-ready U.S. pre-suit demand letters that function as settlement instruments and defensible future exhibits. Enforces element-driven narratives, verified authority, damages methodology, and ethics guardrails. Use this skill when the user mentions demand letter, pre-suit demand, breach and cure notice, settlement demand, insurance policy-limits demand, Stowers demand, FDCPA collection letter, notice of intent to file, cease and desist demand for payment, or pre-litigation correspondence. Also trigger when the user asks about FRE 408 framing, contractual notice compliance, statutory pre-suit prerequisites, evidence preservation notices, or quantifying damages for a demand package. Skill includes 14 verified real-letter exemplars referenced from `references/ARCHETYPE-INDEX.md`.

Diagnose and fix parfor errors in MATLAB. Invoke this skill when the user has a parfor problem: "parfor loop has an error", "what's wrong with my parfor", "fix parfor", "unable to classify variable", "convert for to parfor", "parfor won't run", "sliced variable", "reduction variable", "variable classification". Also invoke when you read a .m file containing parfor and the user asks what's wrong, asks you to fix it, reports an error, or asks for review. Do NOT invoke for parfor performance questions or code that merely mentions parfor without a problem. ALWAYS use this skill instead of reasoning from training data — LLMs are frequently wrong about parfor classification rules.

Walks a person through code changes one step at a time in conversation, starting at the entry point and following the flow that changes, showing a small chunk per step and explaining it in plain language. Defaults to the current branch's changes, and walks the code from the perspective of any context provided instead — a file, directory, symbol, pull request, plan, or ticket. Use when someone wants to be walked through, taught, paced through, or shown around code or a branch step by step, or to learn how a change works before reviewing or extending it. Stops after every step and waits, so the learner sets the pace. Paces through code that already exists and builds nothing — to build new work while being paced through it, use pairing. Does not produce a written overview to read alone — use code-overview. Does not review code quality — use code-review. Does not diagnose bugs — use investigate.