5,773 resources
io.github.garrytan/gbrain/postgres-adopt
Detect which gbrain engine is in use (PGLite vs Postgres), prefer Postgres for agent-harness installs, install/provision Postgres (Supabase discovery via SUPABASE_ACCESS_TOKEN, local Postgres, opt-in Docker), and move an existing PGLite brain with the guarded engine migration. Detection is one engine-free command; the install ladder is one flag.
io.github.NousResearch/hermes-agent/publish-site
Versioned site deploys to GitHub/Cloudflare/Netlify Pages.
io.github.NousResearch/hermes-agent/agentmail
Use when an agent needs AgentMail CLI email inboxes.
io.github.PostHog/posthog/exploring-llm-traces
Debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against `events.properties.$ai_input` / `$ai_output_choices` returns empty — message content lives only on the dedicated `posthog.ai_events` table.
io.github.PostHog/posthog/maintaining-python-tests
Maintains existing pytest and Django test suites without weakening correctness. Use when asked to reduce Python test runtime or CI work, investigate slow pytest families, remove stale migration tests, consolidate repeated setup, improve Python test ownership, or measure whether a test optimization worked after merge. Ranks work by measured cost, applies the writing-tests value gate to existing coverage, preserves distinct behavior cases, validates isolation after shared-fixture changes, and separates testcase work from pytest-suite wall time. For an intermittent failure, use fixing-flaky-tests instead.
io.github.CherryHQ/cherry-studio/code-mate-hermes
Runs Hermes Agent in one-shot mode for bounded local tasks. Use when the user explicitly asks to delegate work to Hermes and accepts its automatic tool approval behavior.
io.github.openclaw/openclaw/clawdtributor
Clawtributor PRs here, last week or another window: discover conversation refs, recheck GitHub, rank by impact.
io.github.addyosmani/agent-skills/frontend-ui-engineering
Builds production-quality, accessible, responsive user-facing UIs. Use when building or modifying interfaces and pages, creating components, implementing layouts, meeting WCAG accessibility requirements, managing state, or when the output needs to look and feel production-quality rather than AI-generated.
vcanary
io.github.lobehub/lobehub/compose-atoms
Decompose a heavy domain feature into mountable capability atoms. Use when a Viewer/Page/index.tsx owns fetch, filters, mutations, modals, and host integrations together; a visual split still leaves store calls and actions on the page; a portal, embed, share, mobile, or micro-app needs a subset of the same domain; or a new capability is landing as another `readOnly`/`mode`/`variant` flag. Triggers on `compose-atoms`, sink state, 状态下沉, 重业务拆分, 拆成原子, 原子组件, 组装, god component, fat viewer, module graph, slot composition, host seam.
io.github.sickn33/agentic-awesome-skills/ida-reverse
Reverse engineer binaries with IDA Pro: decompilation, disassembly, data-flow tracking, cross-references, and IDA MCP automation for deep static analysis of PE/ELF/Mach-O targets.
io.github.sickn33/agentic-awesome-skills/firmware-pentest
Firmware penetration testing following the OWASP FSTM nine-stage flow: extraction, EMBA automation, Firmadyne/QEMU emulation, AFL++ fuzzing, and hands-on exploitation in authorized labs.
io.github.sickn33/agentic-awesome-skills/reverse-browser-automation
Automate browsers (Playwright) and Windows desktop applications (UI automation) for reverse-engineering evidence collection, UI-driven workflows, and network observation during analysis.
io.github.sickn33/agentic-awesome-skills/src-hunter
Bug-bounty/SRC vulnerability-hunting workflow: five-phase methodology (intake, recon, enumeration, hunt, report) with attack playbooks for SQLi, XSS, RCE, SSRF, IDOR, CSRF, path traversal, and file upload.
io.github.sickn33/agentic-awesome-skills/pentest-tools
Operate 20+ penetration-testing tools (Nmap, Nuclei, SQLMap, FFUF, Hashcat, and more) through structured workflows with consistent output handling.
io.github.sickn33/agentic-awesome-skills/mobile-reverse
Authorized Android/iOS application reverse engineering and security testing: APK/IPA analysis, runtime instrumentation (Frida/Objection), SSL-pinning and jailbreak/root-detection bypass, per OWASP MASTG.
io.github.sickn33/agentic-awesome-skills/docs-generator
Generate technical deliverables from completed analysis: reverse-engineering reports, penetration-test reports, CTF write-ups, and signature-analysis documentation with evidence-backed structure.
io.github.sickn33/agentic-awesome-skills/cloud-k8s
Authorized cloud, container, and Kubernetes security assessment: metadata SSRF, IAM misconfiguration, container escape paths, and cluster RBAC review.
io.github.sickn33/agentic-awesome-skills/pwn-chain
Go from reverse engineering to a working exploit: stack/heap/kernel pwn workflows with pwntools, libc-database, ROP, and stabilization from CTF to authorized remote targets.
io.github.sickn33/agentic-awesome-skills/database-security
Authorized database security assessment across PostgreSQL, MySQL, MSSQL, MongoDB, and Redis: exposure, authorization gaps, UDF/command execution paths, and misconfiguration review.
io.github.apple/container/container
Use when running, building, or managing Linux containers on macOS, or when a task involves Docker, docker compose, Lima, Colima, or Podman commands on a Mac, Dockerfiles, OCI images, image registries, or setting up a Linux development environment on Apple silicon.
io.github.getsentry/sentry/feature-flags
Gate a Sentry feature behind a FlagPole feature flag. Use when adding a feature flag, registering a flag in temporary.py, checking a flag from Python or the frontend, enabling a flag in tests, or asking where FlagPole rollout config lives. Trigger on "add a feature flag", "gate this behind a flag", "register a flag", "features.has", "api_expose", "OrganizationFeature", "ProjectFeature", "FlagPole".
io.github.PostHog/posthog/formatting-insight-axes
Pick the right y-axis unit when creating or updating an insight via `posthog:insight-create` or `posthog:insight-update` — both TrendsQuery (`trendsFilter.aggregationAxisFormat`) and SQL insights (`DataVisualizationNode`, `chartSettings.yAxis[].settings.formatting`). Use when the agent is about to add a `formula` purely to convert units (e.g. dividing seconds by 60 to display minutes), when a `math_property` or SQL column is a duration, currency, ratio, or large count, or whenever the user mentions "format the y-axis", "duration", "seconds", "minutes", "hours", "milliseconds", "ms", "percentage", "%%", "currency", "decimals", "axis label", or "axis unit" in the context of a graph insight.
io.github.bentoml/BentoML/bentoml-deploy-scriptgen
Generate a standalone, committable production deploy-script bundle (deploy/deploy.py + one config.yml — overrides only — from which the Kubernetes manifests are rendered) that builds, containerizes, pushes, deploys, and verifies a BentoML service without any agent involved — runnable from a terminal or CI/CD. Use when the user says things like "generate a deployment script", "deploy from CI/CD", "set up a production deployment pipeline", "deploy without the agent", "give me a script I can commit to deploy this", or "automate my BentoML deploys". Complements the interactive skills: bentoml-containerize, bentoml-k8s-deploy, and bentoml-ec2-deploy do a one-off deploy with you in the loop; this skill emits scripts that repeat it forever. Kubernetes and EC2 targets.
io.github.bentoml/BentoML/bentoml-ec2-deploy
Deploy a containerized BentoML service directly onto one or more plain AWS EC2 instances with Docker — no Kubernetes. Takes a pushed container image (built by the bentoml-containerize skill), either uses the user's existing instances over SSH or provisions a new instance via the AWS CLI (SSM AMI lookup, security group, key pair), runs the container with restart-on-reboot, and verifies with a real inference request. Use when the user says things like "deploy my BentoML service to EC2", "run my bento on an AWS VM", "deploy this bento image to an EC2 instance", "run my BentoML container on AWS without Kubernetes", or "put my bento on a cloud VM". For Kubernetes targets use bentoml-k8s-deploy instead.
