691 skills · sorted by relevance
Use this skill to track recent and upcoming SMS methodology changes from FMCSA. Covers proposed rule changes, comment periods, and implementation timelines.
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Use this skill when the user asks about FMCSA Compliance Safety Accountability (CSA) program scoring — the seven BASIC categories, BASIC Severity Indicator (BSI), peer percentiles, intervention thresholds, how violations age out, SMS Methodology, ISS (Inspection Selection System), what "alert" status means, Safety Measurement System mechanics, or DataQ disputes. Cite SMS Methodology v3.20.
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Cognitive Systems Engineering design methodology for joint cognitive systems and human-centered automation
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Methodology for eliciting expert knowledge about cognitive processes, decisions, and mental models in complex tasks
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Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.
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Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.
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Deep-dive offensive methodology for dependency confusion and namespace attacks across all major package ecosystems. Covers npm scope confusion exploiting the gap between public and private scoped packages and .npmrc misconfigurations where registry mappings fail to pin internal scopes exclusively. Addresses PyPI namespace attacks through --extra-index-url resolution ordering, NuGet feed priority exploitation when multiple package sources are configured without clear directives, Maven and Gradle repository ordering where artifact resolution traverses repositories sequentially, Go module proxy abuse through GOPROXY misconfiguration, Ruby gems namespace squatting, and Docker image tag confusion with unqualified image references. Provides complete proof-of-concept methodology using safe callbacks including DNS canary via interactsh or Burp Collaborator and HTTP beacon with no destructive payload. Covers reconnaissance techniques for discovering internal package names through GitHub repository analysis, error ...
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Cross-cutting methodology for the CVE-hunting pipeline (ported from find-cve-agent): when a bug is design-vs-real, false-positive avoidance, version checking before reporting, responsible disclosure norms, acceptance-rate expectations per vuln class, vulnerability chaining playbook, known false-positive patterns, maintainer-response patterns, secure-pattern reference (what NOT to flag), and a self-criticism checklist to run before claiming a finding. Use this alongside the cve-hunter/cve-exploiter/cve-validator agents and the /cve-hunt pipeline — load it whenever judging if a finding is real, worth reporting, or already fixed.
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This skill should be used when the user asks to "learn ethical hacking", "understand penetration testing lifecycle", "perform reconnaissance", "conduct security scanning", "exploit vulnerabilities", or "write penetration test reports". It provides comprehensive ethical hacking methodology and techniques.
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Investigate photos, videos, public accounts, and records using OSINT, research based on public information. Connect sparse clues, test competing explanations, and report evidence, confidence, and unresolved questions about locations, events, identities, or infrastructure relationships.
Assess a scoped competitor set across nine dimensions using evidence and consistent scoring criteria. Produce comparable profile cards and a two-axis positioning plot to show rival strengths, weaknesses, and competitors occupying the client's target space.
Build and review Python tests with pytest. Use reusable setup, simulated external services, and multiple input cases to check behavior, then measure coverage—the proportion of code exercised by tests—and follow a test-first development cycle.
Write and run Perl tests with Test2::V0 or Test::More, replace external dependencies with controlled test substitutes, and measure coverage—the code exercised by tests. Guidance covers test-first development, failure diagnosis, and migration between testing modules.
Write Rust unit, integration, and async tests using a test-first workflow. Covers generated test inputs, mock dependencies, performance benchmarks, and coverage checks to assess behavior and identify code that remains untested.
Write Kotlin tests with Kotest and MockK using test-driven development, where tests come before implementation. Cover asynchronous code, check behavior across generated inputs, and use Kover reports to measure how much code the tests exercise.
Add machine learning to an existing application through a structured workflow. The skill guides problem definition, data checks, separation from business logic, and training and evaluation of a simple initial model.
Helps build Go tests using a test-first workflow. Covers grouped test cases, HTTP response checks, performance benchmarks, fuzzing to explore generated inputs, and coverage reports showing which code was exercised by tests.
Write Django and Django REST Framework tests with pytest-django, starting with tests before implementation. Covers test data generation, simulated external services, model and API checks, and coverage reports showing which code the tests exercise.
Compare competitors using evidence-backed scores for positioning, brand expression, services, credibility, and other dimensions. Produce consistent profile cards and a two-axis chart showing which rivals occupy the market position your client wants to claim.
Turn academic papers into structured reviews that explain findings, strengths, weaknesses, and research methods. Assess originality and supporting evidence, compare relevant literature, and provide specific improvement suggestions in a detailed review or brief summary.
Research a topic across multiple web sources before writing articles, reports, or presentations. Gather facts, examples, and expert views, then check current information, competing perspectives, and limitations to build a well-supported understanding.
Organize new features, complex implementations, and architecture changes into the SPARC stages: specification, pseudocode, architecture, refinement, and completion. Define requirements and acceptance criteria, consult existing patterns, and record lessons for future work.
Use Reasonix to build or improve a Python command-line interface for existing software. The workflow covers source analysis, interactive commands, JSON output, tests, documentation, and installation checks using the software’s own backend where possible.
Build and improve Python command interfaces for GUI applications with OpenClaw. The skill guides source analysis, command design, testing, and packaging, including interactive sessions and structured JSON output for agents.