Results for “find”

Match a user request to the right skill using trigger matching, project context, and conversation history. Score ambiguity 1–10 and resolve it using context signals or a single clarifying question. Load when project-orchestrator needs to route a request, or when multiple skills could match and the right one is unclear. Also triggers on "which skill should handle this", "route this request", "I'm not sure which skill to use", "disambiguate this", "skill routing". Called by project-orchestrator for request routing. For finding whether a skill exists for a capability, use skill-finder instead.

Runs the current git diff through the Codex CLI for an independent, read-only second-opinion review, then triages findings back into the session for Claude to apply fixes.

Creates a GitHub pull request with an auto-generated summary, running mandatory pre-commit and code-quality review gates, plus optional deep-review subagents, and posts findings into the PR body.

Review MATLAB code for quality, performance, maintainability, and adherence to MathWorks coding standards. Uses check_matlab_code and matlab_coding_guidelines. Use when reviewing code, checking style, finding code smells, assessing quality, or preparing code for handoff or publication.

Search and analyze news articles with media bias context, get balanced perspectives, access live stock/ETF/crypto data with AI analysis, and find memes by semantic meaning.

Analyzes a multi-step conversion funnel in Adobe Customer Journey Analytics to find where users drop off and which steps have the worst leakage.

Reviews a PR or diff through three independent lenses—correctness, security, and test coverage—then deduplicates and validates findings before persisting them, reducing noise while catching more real bugs.

Analyzes Perfetto traces to find the root cause of latency, memory, or jank issues in Android apps. Use when the user provides a Perfetto trace file and asks any question, ongoing investigation, or open-ended request to analyze its contents.

Skeptical pushback on the research dossier before it feeds the outline. Asks whether claims are cited, whether surprising findings are actually surprising, whether we missed the strongest competitor angle. One revision pass on FAIL (BLOG_AGENT_RESEARCH_REVISION_BUDGET, default 1).

Diagnose frame-rate bottlenecks (CPU vs GPU bound FIRST), control Unreal Insights traces, sample live frame times, and annotate performance captures. Use when FPS is low/bad, the game is slow, or you need to find what is limiting the frame rate.

Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.

Searches for existing content pages containing a specific block in AEM Edge Delivery Services, reporting URLs with occurrences and variants to help identify test content during development.

Performs STRIDE-A threat model analysis of repositories and systems, producing architecture overviews, DFD diagrams, prioritized findings, and executive assessments. Supports both single analysis and incremental updates with change tracking.

Routes to 82 Lightning Web Components skill packages covering reactivity, wire adapters, component communication, accessibility, performance, security, and Jest testing. Finds and opens the exact skill package for LWC-related requests.

Use this skill when the user asks to "analyze my content", "learn my writing style", "research competitors", "find content angles", "improve my blog", "write like me", "embody my brand voice", or mentions content strategy, voice analysis, competitive research, or iterative content improvement.

Creative discovery partner for ideation and concept shaping. Use when brainstorming, exploring product ideas, expanding vague concepts, comparing directions, finding opportunities, creative ideation. Triggers: 'brainstorm', 'product ideas', 'concept exploration', 'idea expansion', 'creative thinking', 'product discovery', 'what if', 'I have an idea'.

HYDRA v2.0 — Squeeze Detector. Finds crowded trades about to unwind. Funding extreme + SM positioned against the crowd + price starting to move. Goes opposite to the funding crowd. Only liquid assets ($20M+ volume). DSL exit managed by plugin runtime via runtime.yaml.

Deep research across Reddit, X/Twitter, Hacker News, YouTube, Polymarket, and the web from the last 30 days. Synthesizes findings into a grounded, cited briefing. Use when: researching trends, doing competitive analysis, answering 'what's the latest on X', or comparing tools/products.

Place trusted contextual information into prompts or agent state using explicit boundaries, provenance, and templates. Use when relevant context has already been selected and must be inserted safely; use context-retrieval to find it or context-optimization to choose and order it.

Runs a three-layer verification pipeline on AI-generated output: extracts verifiable claims, finds supporting or contradicting sources via web search, and produces a structured verification report with source links for human review.

Audits trending open-source repos for exploitable vulnerabilities using Semgrep, TruffleHog, osv-scanner, and Slither, then routes findings to responsible disclosure channels (PVR for code flaws, public PRs for dependency CVEs).

Automatically discovers social media profiles for individuals or brands across platforms like Facebook, Twitter, Instagram, LinkedIn, and TikTok, returning profile URLs, follower counts, and bio snippets as a downloadable CSV.

Correlate findings across OSINT sources—username enumeration, email lookups, social media profiles, domain records, breach databases, and dark-web mentions—into unified intelligence profiles with confidence scoring and link analysis.

Audits a codebase's animation and motion code, then produces a prioritized list of findings and self-contained implementation plans for other agents to execute.

Runs a full retrospective and dev cycle analysis, chaining /recall and /new-features to reconstruct what went wrong, extract lessons learned, identify rework patterns, and synthesize feature ideas from the findings.

Verifies a claim by finding primary sources and rates the evidence as supported, mixed, unsupported, or unverifiable, with citations and what would change the verdict.

Vendor-neutral framework for scoring software health, estimating technical debt, assessing cloud readiness and open-source safety, and communicating quality to business stakeholders. Use when you need to quantify code health at the application or portfolio level rather than fix individual findings.

Use when a skill needs to open a project as a new herdr workspace via herdr-project, or close a herdr tab it no longer needs. Covers herdr-project open's --worktree and --prompt flags, and finding a tab by label to close it.

This skill should be used when the user asks to "fix pipelines", "fix CI", "check staging pipelines", "fix failing workflows", "fix failing actions", or wants to find and fix failing GitHub Actions workflows on the staging branch of the babysitter repo.

This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.

Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

Compare an existing article's section coverage to the current SERP (Semrush phrase_organic + Firecrawl page extraction) and to the related-term / question landscape (phrase_related + phrase_questions). Find topics ranking pages cover that the article doesn't, and propose new sections.

Analyze datasets to answer defined questions through statistical methods, trend identification, hypothesis testing, and correlation analysis. Use when the user needs evidence-backed findings or decisions from data; use exploratory-data-analysis instead for open-ended first-pass profiling before questions are defined.

Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

Do NOT invoke this skill as a routine first step. Do NOT invoke when any installed skill (see the available skills list) can handle the task, or when general capabilities (writing, coding, analysis, translation, web search) suffice. Invoke ONLY in two cases: (1) the user explicitly asks to find, install, or browse skills / the skill marketplace; (2) no installed skill fits AND the task very likely needs a dedicated skill to be done well (e.g. a specialized file format, a vertical platform workflow). Searches the official QwenWork marketplace, the skills.sh community library, and enterprise skill markets (if available via MCP).

Aggregate security findings across AWS accounts, enable compliance standards like CIS and PCI DSS, configure automated remediation with EventBridge and Lambda, and create custom security insights for organizational risk management.