Live end-to-end test of BearDrive's agent-first onboarding: run the real paste-prompt flow in a fresh headless Claude session (and optionally a full role-played user conversation) against a seeded local hub, asserting the scope hard-gate and hooks-via-init behaviors, and report transcripts plus doc-vs-reality…
The conventions this team actually follows — naming, branching, review, and the decisions someone already made so nobody re-litigates them. Use when writing or reviewing code here, opening a PR, or when unsure "how do we do this".
Evaluates an existing context structure for completeness, level appropriateness, and best practices. Checks whether your context is well-formed, not whether it matches the codebase.
Analyzes an existing project and generates context files (AGENTS.md, context directory, cascading structure) pre-populated with discovered information. Run once to bootstrap, then customize.
Guides transition from your current context level to the next one -- minimal to full, full to cascading, or adding skills and hooks layers. Preserves existing content.
Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, agent browsing…
Design and implement comprehensive evaluation systems for AI agents. Use when building evals for coding agents, conversational agents, research agents, or computer-use agents. Covers grader types, benchmarks, 8-step roadmap, and production integration.
Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.
Use before implementation, fix, refactor, tests, file creation, or project-structure changes to decide placement, reuse, ownership, and maintainable integration before editing.
A writing workflow for the top section of a landing page: the headline, supporting text, proof, and call to action. It evaluates positioning and tests multiple headline options for clarity and distinctiveness.
A research workflow for investigating a topic with language models while checking for bias and preserving the results in a knowledge base. It covers broad learning research and research for choosing between options.
Create new skills, modify and improve existing skills, and measure skill performance. Use when the user wants to capture a workflow as a reusable skill, draft a new skill, edit or optimize an existing one, run evals against a skill, or tune a skill's description for triggering accuracy.
Architectural thinking. Load when crossing component boundaries, reshaping contracts, introducing a service, or deciding where code belongs. Covers naming boundaries, locating code by ownership and change cadence, changing contracts carefully, measuring coupling and cohesion, and not abstracting until the pattern is…
Engineering discipline for AI harnesses. Nine principles for how an LLM coding agent should write code - think before coding, push back when warranted, plan inline, simplicity first, surgical changes, goal-driven execution, know when to stop, calibrate confidence, maintain context. Load as the default for any coding…
Penetration-testing discipline for authorized security assessment. Load to audit a codebase or system you own (or are explicitly authorized to test) for vulnerabilities - then fix them with secure. Also covers scoped engagements: red-team exercises, CTFs, bug-bounty targets. Covers confirming authorization and scope…