Results for “find”

Reviews staged and unstaged code changes for security vulnerabilities, code quality issues, and adherence to project conventions, reporting findings by severity.

Run GGUF models locally with llama.cpp, including finding the right file on the Hugging Face Hub, installing, quantizing, serving, and using Python bindings.

Runs three parallel reviewers to find code reuse, quality, and efficiency issues in a diff, then applies safe fixes and reports risky ones.

Web search and research using Perplexity AI. Use when user says "search", "find", "look up", "ask", "research", or "what's the latest" for generic queries. NOT for library/framework docs (use Context7) or workspace questions.

PdfLatex is a tool that converts Latex sources into PDF. This is specifically very important for researchers, as they use it to publish their findings. It could be installed very easily using Linux te

Structures user research — planning sessions, synthesizing findings, and translating insights into product decisions. Use when planning user interviews, analyzing research data, creating personas or journey maps, or turning qualitative feedback into structured recommendations.

Fast, accurate code search for AI agents using ~98% fewer tokens than grep+read. Indexes any local or remote repository in under a second (~250ms on CPU, no GPU or API key needed). Supports natural-language and symbol queries, semantic similar-code discovery, and MCP server integration for Claude Code, Codex, Cursor, and OpenCode. Python library available for programmatic use. Triggers on: semble, code search, semantic code search, semble search, token-efficient search, find code, code search mcp, agent code search, semble find-related, semble savings.

Scan and audit AI agent skills for security risks before installation, producing a PASS/WARN/FAIL verdict with findings and remediation guidance.

Queries Hugging Face benchmark leaderboards to find the best AI models for a task, filters by device constraints, and returns a ranked comparison table with scores.

Research any topic across Reddit, X, and the web from the last 30 days, then synthesize findings into actionable insights and copy-paste-ready prompts.

Finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery, with channel-specific outreach drafting.

Finds architectural friction in a codebase and proposes deep-module refactors to improve testability and AI-navigability, informed by the domain glossary and ADRs.

This skill should be used when searching, fetching, or downloading emails from Gmail. Use for queries like "search Gmail for...", "find emails from John", "show unread emails", "emails about project X", or "download attachment from email".

ISO 13485 internal audit expertise for medical device QMS. Covers audit planning, execution, nonconformity classification, and CAPA verification. Use for internal audit planning, audit execution, finding classification, external audit preparation, or audit program management.

Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review.

Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.

Two-phase Apollo.io prospecting: free People Search to discover ICP-matching leads, then selective enrichment to reveal emails/phones (credits per contact). Deduplicates against CRM (Google Sheets). Supports Apollo API and MCP tools.

SLURM EDF job must use marker names from CHANNELNAMES.txt for output files. Trigger: EDF output naming, SLURM job producing CH#_edf.tif instead of marker names, downstream _find_edf_file failures

Probe the forms on a page for validation gaps — missing required-field enforcement, no client-side validation, accepts malformed input, and absent error messaging. Reports per-field findings. Playwright MCP only, no signup.

Encodes Emil Kowalski's design engineering philosophy — UI polish, component design, animation decisions, and the invisible details that make software feel great. Use as the reference/philosophy layer for interaction craft. For the narrower, task-specific pieces of this same philosophy, see `animate` (build one animation), `review-animations` (review a diff), `improve-animations` (audit a codebase), `find-animation-opportunities` (find what's missing motion), `animation-vocabulary` (name an effect), `apple-design` (gesture/spring physics), `ask-sonner` (the Sonner toast library). Source: github.com/emilkowalski/skills.

Audits Convex performance for reads, subscriptions, write contention, and function limits. Use for slow features, insights findings, OCC conflicts, or read amplification.

Perform a read-only, defect-first review of a specified code change and return every actionable finding. Use when another agent delegates review of uncommitted changes, a base-branch diff, a commit, or custom review instructions.

Retrieve task-relevant project and global memory without loading everything. Load when the user asks what we decided, recall prior context, find memory about a feature, explain past rationale, resume a task, or check deferred ideas.

SEO topic research on fb.ai — find keyword clusters for a topic, deep-analyze their SERPs, and turn the winners into a scheduled content plan. Hands the resulting article briefs to article-publisher. Run on demand.

Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.

Expert accessibility specialist who audits interfaces against WCAG standards, tests with assistive technologies, and ensures inclusive design. Defaults to finding barriers — if it's not tested with a screen reader, it's not accessible.

Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.

Android APK decompiler that converts DEX bytecode to readable Java source code. Use when you need to decompile APK files, analyze app logic, search for vulnerabilities, find hardcoded credentials, or understand app behavior through readable source code.

Review the current git diff for correctness, security, and performance risks before you commit. Produces severity-ranked findings with the exact file:line and a suggested fix. Pure Claude Code — no MCP, no signup.

NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.

Orients researchers to an unfamiliar academic field by searching Consensus, building a strategic search plan with PICO or other frameworks, and synthesizing findings into a formatted Word document.

Finds the least-privilege Azure RBAC role for an identity, generates CLI commands and Bicep code to assign it, and provides guidance on permissions needed to grant roles.

Assesses NumPy code for cuPyNumeric migration readiness by analyzing source code against an API support manifest and GPU-scaling idioms, producing a structured verdict with per-finding reasoning.

Audits .NET test mock usage by tracing each mock setup through the production code's execution path to find dead, unreachable, redundant, or replaceable mocks.

Access Expo's official library of ~70 integration examples to find canonical patterns for adding third-party services like Stripe, Clerk, Supabase, and maps to your Expo app.

Perform authorized penetration testing against AWS, Azure, and GCP cloud environments using cloud-specific tools and methodologies, with findings mapped to the MITRE ATT&CK Cloud matrix.