Vexor CLI
Semantic file discovery via `vexor`. Use whenever locating where something is implemented/loaded/defined in a medium or large repo, or when the file location is unclear. Prefer this over manual browsing.
MCP get_skill({ skillId: "vexor-cli-skill-90774fc5" })Use this skill with your agent
Create a free account and connect via MCP
# Vexor CLI Skill ## Goal Find files by intent (what they do), not exact text. ## Use It Like This - Use `vexor` first for intent-based file discovery. - If `vexor` is missing, follow [references/install-vexor.md](references/install-vexor.md). ## Command ```bash vexor "<QUERY>" [--path <ROOT>] [--mode <MODE>] [--ext .py,.md] [--exclude-pattern <PATTERN>] [--top 5] [--format rich|porcelain|porcelain-z] ``` ## Common Flags - `--path/-p`: root directory (default: current dir) - `--mode/-m`: indexing/search strategy - `--ext/-e`: limit file extensions (e.g., `.py,.md`) - `--exclude-pattern`: exclude paths by gitignore-style pattern (repeatable; `.js` → `**/*.js`) - `--top/-k`: number of results - `--include-hidden`: include dotfiles - `--no-respect-gitignore`: include ignored files - `--no-recursive`: only the top directory - `--format`: `rich` (default) or `porcelain`/`porcelain-z` for scripts - `--no-cache`: in-memory only, do not read/write index cache ## Modes (pick the cheapest that works) - `auto`: routes by file type (default) - `name`: filename-only (fastest) - `head`: first lines only (fast) - `brief`: keyword summary (good for PRDs) - `code`: code-aware chunking for `.py/.js/.ts` (best default for codebases) - `outline`: Markdown headings/sections (best for docs) - `full`: chunk full file contents (slowest, highest recall) ## Troubleshooting - Need ignored or hidden files: add `--include-hidden` and/or `--no-respect-gitignore`. - Scriptable output: use `--format porcelain` (TSV) or `--format porcelain-z` (NUL-delimited). - Get detailed help: `vexor search --help`. - Config issues: `vexor doctor` or `vexor config --show` diagnoses API, cache, and connectivity (tell the user to set up). ## Examples ```bash # Find CLI entrypoints / commands vexor search "typer app commands" --top 5 ``` ```bash # Search docs by headings/sections vexor search "user authentication flow" --path docs --mode outline --ext .md --format porcelain ``` ```bash # Locate config loading/validation logic vexor search "config loader" --path . --mode code --ext .py ``` ```bash # Exclude tests and JavaScript files vexor search "config loader" --path . --exclude-pattern tests/** --exclude-pattern .js ``` ## Tips - First time search will index files (may take a minute). Subsequent searches are fast. Use longer timeouts if needed. - Results return similarity ranking, exact file location, line numbers, and matching snippet preview. - Combine `--ext` with `--exclude-pattern` to focus on a subset (exclude rules apply on top).
Related Skills
More skills in Software Engineering
Accessibility Standards
Comprehensive web accessibility standards based on WCAG 2.2 AA, with 38+ anti-patterns, legal enforcement context (EAA, ADA Title II), WAI-ARIA patterns, and framework-specific fixes for modern web frameworks and libraries.
Accord
Authoring unified specification packages across Business/Development/Design teams via staged elaboration (L0 Vision → L1 Requirements → L2 Team Detail → L3 Acceptance Criteria). No code. Use when authoring cross-team specs, building L0-L3 packages, or aligning Biz/Dev/Design on a single source of truth.
Acquire Codebase Knowledge
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
Acreadiness Assess
Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
Acreadiness Generate Instructions
Generate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
Acreadiness Policy
Help the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
Explore Other Categories
Skills from other categories with shared topics
Airunway Aks Setup
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
Alz Accelerator
Deploy Azure Landing Zones using the ALZ Accelerator with AVM (Azure Verified Modules). Use this skill whenever the user mentions Azure Landing Zones, ALZ, Azure landing zone accelerator, AVM modules for landing zones, deploying management groups, hub-and-spoke networking, Virtual WAN, platform landing zones, or asks about Bicep vs Terraform for Azure infrastructure. Also trigger when the user wants to bootstrap CI/CD for Azure platform deployment, set up management groups hierarchy, or deploy connectivity/identity/management platform subscriptions.
Azure Aigateway
Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.