「find」の検索結果

Queries S3 object metadata, tracks bucket activity, audits object changes, searches annotations, and analyzes storage metrics using S3 Metadata system tables (journal, inventory, annotation) and S3 Storage Lens tables via Athena SQL. Applies when counting objects, finding recent uploads or deletions, identifying who wrote to a prefix, breaking down storage classes, finding objects by tag, searching annotation content, analyzing storage lens metrics, or enabling S3 Metadata tracking. Prefers system tables over raw S3 APIs (list-objects-v2, head-object) at scale. Trigger phrases: bucket activity, object count, who uploaded, track deletions, storage class breakdown, find by tag, search annotations, storage lens metrics, audit bucket changes.

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Run an AWS Security Agent scan on the workspace — uploads the source to AWS, scans it with the managed Security Agent service, and returns ranked, verified findings with code locations and remediations. Use when the user asks to scan code, find vulnerabilities, run a security scan or review, check security issues, check scan status, show findings, list recent scans, or stop a scan.

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Run an AWS Security Agent penetration test against a live web application — registers and verifies the target domain, exercises the supplied endpoints with the managed Security Agent service, and returns verified runtime findings. Use when the user asks to pentest, run a penetration test, test their app's attack surface, find runtime vulnerabilities, register or verify a target domain, or check pentest status / findings.

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Resolve data lake and lakehouse asset references across Glue Data Catalog, S3, S3 Tables, and Redshift. Triggers on: find the table, where is our data, which table has, locate dataset, find data for, search catalog, what tables match, Redshift table, lakehouse table, data lake table, warehouse table, reverse lookup S3 path. Do NOT use for: full catalog audits (use exploring-data-catalog), running queries (use querying-data-lake), creating tables (use creating-data-lake-table).

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Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.

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Audits a website, app, onboarding flow, or design for usability, conversion, and product problems, tying every finding to a business outcome and a severity. Use this to review an interface, diagnose low conversion or activation, find where users drop off, get structured product feedback, or decide which UX fixes to ship first. For raising visual quality rather than finding problems, use `interface-craft`.

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Identifies translationally meaningful paths for bioinformatics findings by mapping omics or computational discoveries to diagnosis, stratification, prognosis, treatment-response, monitoring, or target-nomination use cases, while auditing bridge evidence, assayability, and validation burden. Use this skill when a user wants to know whether a bioinformatics finding can be framed as a stronger translational topic without overclaiming clinical relevance. Always separate statistical signal from translational value, and never imply clinical utility, targetability, or validation depth without explicit evidence support.

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Composes a Discussion around key findings, mechanisms, clinical relevance, and limitations. Use when writing or improving a Discussion section for any biomedical manuscript — including interpreting results, connecting to prior literature, addressing unexpected findings, framing limitations, and writing the conclusion. Also triggers on "write my discussion", "help me discuss my findings", "how do I compare to prior studies", "write the limitations paragraph", or "draft a discussion for my paper".

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Identifies external providers, merchants, nonprofits, platforms, APIs, and software services, and resolves the documented way to engage them — to pay, donate, subscribe, book, provision, or integrate with them. MUST be used BEFORE web search, model memory, or any other directory/vendor-lookup skill for ANY request that requires selecting, finding, or engaging an external provider or organization — whether a specific one is named ("pay my Twilio bill"), implied ("donate to the Red Cross"), or only a category is stated ("set up a mailing client", "I need a database", "find me a CRM"). Directory supplies verified recipients and their documented payment or engagement path. Results are ranked by agent-readiness (how reliably an agent can complete the interaction), relevance and popularity. Do not treat this as a search-only tool. Examples: "setup a database", "find hosting", "pay X", "use Twilio", "donate $50 to the Red Cross", "book a table at Y", "subscribe to a CRM".

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Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.

日本語の概要は準備中です。原文の説明を表示しています。

Turn an incoming set of findings — from an AI reviewer, a referee report, a code review, a linter, or a second model — into verified fixes, without letting a confident misread damage correct work. Every finding is a CANDIDATE until checked against the actual source. Use whenever you receive review comments, audit findings, or a critique you did not write yourself, especially when the reviewer is a model or when the volume is too large to check by feel.

日本語の概要は準備中です。原文の説明を表示しています。

List, find, and show Azure resources across subscriptions or resource groups. Handles prompts like "list the websites in my subscription", "list my web apps", "show my app services", "list virtual machines", "list my VMs", "show storage accounts", "find container apps", and "what resources do I have". USE FOR: list websites, list web apps, list app services, show websites in subscription, resource inventory, find resources by tag, tag analysis, orphaned resource discovery (not for cost analysis), unattached disks, count resources by type, cross-subscription lookup, and Azure Resource Graph queries. DO NOT USE FOR: deploying/changing resources (use azure-deploy), cost optimization (use cost-optimization from the optional azure-cost plugin), or non-Azure clouds.

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Discovers, searches, and installs skills from multiple AI agent skill marketplaces (400K+ skills) using the SkillKit CLI. Supports browsing official partner collections (Anthropic, Vercel, Supabase, Stripe, and more) and community repositories, searching by domain or technology, and installing specific skills from GitHub. Use when the user wants to find, browse, or install new agent skills, plugins, extensions, or add-ons; asks 'is there a skill for X' or 'find a skill for X'; wants to explore a skill store or marketplace; needs to extend agent capabilities in areas like React, testing, DevOps, security, or APIs; or says 'browse skills', 'search skill marketplace', 'install a skill', or 'what skills are available'.

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Fix a team's chat sprawl — the channel map with one purpose per channel, the naming scheme that makes purpose findable, the archive pass for the dead and duplicated, and the posting norms (threads, @-discipline, urgency signals) that keep signal findable. Use when asked clean up our Slack/Teams, we have 90 channels and nothing is findable, set channel norms, or where should things get posted. Produces the channel audit and map, the naming scheme, the norms card, and the archive pass.

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Design the archive layer that keeps current workspaces lean without losing history — what moves, when, to where, findable-by-search, with the project-close ritual that makes archiving automatic instead of aspirational. Use when asked set up an archiving system, our workspace is drowning in old projects, when should things get archived, or make history findable without cluttering today. Produces the archive triggers, the destination structure, the findability rules, and the close-out ritual.

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Detect Insecure Direct Object Reference (IDOR) vulnerabilities in a codebase using a three-phase approach: recon (find candidates), batched verify (check authorization in parallel subagents, 3 candidates each), and merge (consolidate batch results). Checks endpoints for missing ownership or authorization checks on user-supplied identifiers. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/idor-results.md. Use when asked to find IDOR or authorization bypass bugs.

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Detect Server-Side Template Injection (SSTI) vulnerabilities in a codebase using a three-phase approach: recon (find template rendering sites that use dynamic strings), batched verify (trace user input to those sites in parallel subagents, 3 candidates each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/ssti-results.md. Use when asked to find SSTI or template injection bugs.

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Detect Cross-Site Scripting (XSS) vulnerabilities in a codebase using a three-phase approach: recon (find HTML/JS/DOM sink sites), batched verify (trace user input to sinks in parallel subagents, 3 sink sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/xss-results.md. Use when asked to find XSS or cross-site scripting bugs.

日本語の概要は準備中です。原文の説明を表示しています。

Detect path traversal vulnerabilities in a codebase using a three-phase approach: recon (find file-loading sinks with dynamic paths), batched verify (trace user input and mitigations in parallel subagents, 3 sinks each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/pathtraversal-results.md. Use when asked to find path traversal, directory traversal, or file disclosure bugs.

日本語の概要は準備中です。原文の説明を表示しています。

Detect Server-Side Request Forgery (SSRF) vulnerabilities in a codebase using a three-phase approach: recon (find outbound call sites), batched verify (trace user input to destinations in parallel subagents, 3 sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/ssrf-results.md. Use when asked to find SSRF or server-side request forgery bugs.

日本語の概要は準備中です。原文の説明を表示しています。

Detect SQL injection vulnerabilities in a codebase using a three-phase approach: recon (find unsafe SQL construction sites), batched verify (trace user input to those sites in parallel subagents, 3 sites each), and merge (consolidate batch results). Covers string concat, f-strings, unsafe ORM methods, and dynamic identifiers. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/sqli-results.md. Use when asked to find SQLi or database injection bugs.

日本語の概要は準備中です。原文の説明を表示しています。

Detect insecure file upload vulnerabilities in a codebase using a three-phase approach: discovery (find all upload sites), batched verify (check extension bypass and related issues in parallel subagents, 3 sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/fileupload-results.md. Use when asked to find file upload, unrestricted upload, or extension bypass bugs.

日本語の概要は準備中です。原文の説明を表示しています。

Detect Remote Code Execution (RCE) vulnerabilities in a codebase using a three-phase approach: recon (find dangerous execution sinks), batched verify (trace user input to sinks in parallel subagents, 3 sinks each), and merge (consolidate batch results). Covers OS command injection, eval-like sinks, and unsafe deserialization. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/rce-results.md. Use when asked to find RCE, command injection, or unsafe deserialization bugs.

日本語の概要は準備中です。原文の説明を表示しています。

Detect GraphQL injection vulnerabilities in a codebase using a three-phase approach: recon (confirm GraphQL usage and find unsafe operation document assembly sites), batched verify (trace user input to those sites in parallel subagents, up to 3 candidate sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/graphql-results.md. If no GraphQL technology is found in Phase 1, later phases are skipped. Use when asked to find GraphQL injection, unsafe GraphQL document construction, or operation string injection bugs.

日本語の概要は準備中です。原文の説明を表示しています。