Results for “any-run”

Authenticate, browse pipelines, samples, and projects, upload data, launch pipeline executions, and check run status on any Flow.bio instance via CLI.

Data Version Control for ML projects. Track large datasets and models alongside Git, build reproducible ML pipelines, and run experiments with metric comparison. Works with any storage backend including S3, GCS, Azure, and local filesystems.

Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions.

Systematic audit of all installed tools, packages, and dependencies for available updates. Covers Homebrew, pip, npm/pnpm, system software updates, git repos, and key CLI tools. Run when the user asks "check if anything needs updating" or similar maintenance queries.

Scan codebase and git history for leaked secrets — API keys, tokens, passwords, private keys. Uses whispers-style regex patterns + gitleaks. Run as /leak-check before any git push. Alerts on Claude API keys, GitHub tokens, AWS credentials, and generic high-entropy strings.

ACTIVATE THIS SKILL FOR ANY SHELL COMMAND OR FILE READ. Check curl, wget, rm, sudo, apt, dpkg, chmod, dd, format, powershell, bash, sh. Check pipe patterns like | sh or | bash. Check shell wrappers like bash -c, xargs, find -exec. Check cloud CLI (aws, gcloud, kubectl, terraform). Check when user says sysadmin told me, Stack Overflow says, is this safe, can I run. Block reading of .env, .ssh, .aws, and credential files. This skill blocks dangerous commands and warns on risky ones. ALWAYS apply the safety protocol from this document before responding about any command.

Use for the search-planning stage of a literature review, when database lanes and language layers must be chosen, when DOI coverage policy is unclear, or when inclusion and exclusion criteria need drafting before any source is read; not for running the screening and PRISMA pipeline of a formal review, which is prisma-scoping-review-pipeline.

Execute the full AgentHub competition lifecycle in a single command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.

Provides a sandboxed execution environment for running code, scripts, and automated workflows with resource capping and network isolation.

High-fidelity website/app clone design pipeline. Use when asked to clone, replicate, copy, or recreate any existing website or app screen. Captures real assets (images, SVGs, fonts, colors), extracts exact design tokens, generates the clone in Stitch, composites real assets into the output, then runs section-by-section visual comparison against the reference. Produces 90%+ accurate clones by combining Stitch generation with real asset injection.

Provides a sandboxed execution environment for running code snippets, automating tasks, and deploying logic across multiple languages with resource capping and network isolation.

Run a full pipeline for a task. Orchestrates roles through stages (standalone or HOTL-integrated).

Deploy and manage Cloud Run services, jobs, and worker pools on Google Cloud.

End-to-end game trailer production via the Content Factory trailer_factory pipeline — storyboard → animatic → audio → final cut with inspect-and-fix quality gate. Routes long-running work through Hermes Kanban so it survives pod restarts and exposes a multi-stage status board. Use whenever the user asks for a game trailer, gameplay video, launch teaser, App Store preview video, or any video promoting a game.

A catalog of pollers worth building — the underused half of being a long-running agent. Use when something you care about could change without anyone telling you (a CI pipeline, a competitor's release, a friend's status, a config drift, a dependency CVE) and you want to be the first to notice. Companion to the `pollers` skill, which covers mechanics. This file is the menu of *what's worth polling.*

Produces durable, structured documentation in codebases with sparse docs — readable by humans and agents alike. Two invocation modes — `survey` proposes a doc plan; `draft` writes one specific doc end-to-end with a factcheck-then-persona review gate. Triggered by phrases like "doxcavate this repo", "doxcavate the X", "doxcavate `runbook-deploy`", "what should be documented?", "document this code", "write a how-it-works for X", or any request to inventory or write project documentation.

CRITICAL: This skill MUST be used for ALL SSH operations. NEVER use bash 'ssh' or 'scp' commands directly - always use this skill instead. Triggers: ANY mention of 'SSH', 'ssh', 'remote server', 'connect to server', server IPs (e.g., 192.168.x.x, 10.0.x.x), hostnames (e.g., user@host.com, server.example.com), 'login to', 'upload to server', 'download from server', 'deploy', 'run on server', 'check server', 'server status', 'execute remotely', 'bastion host', 'jump host', '跳板机', '服务器', '远程', '连接', '登录', '上传', '下载', '部署', 'transfer between servers', '服务器间传输', '迁移', 'migrate', 'server to server'. If user mentions ANY server operations or provides server connection details, use this skill. This skill provides daemon-based persistent connections, connection pooling, jump host support, server-to-server transfer, automatic error recovery, and significant performance boost. DO NOT use for: local commands, localhost, current directory operations.

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in...

Dispatch coding work across the intelli-verse-x gastown swarm — pick the right rig (gastown, hermes-agent, content-factory, intelli-verse-kube-infra, intelliverse-x-games-platform-2, the SDKs, nakama, leantime, etc.), spawn or assign to a polecat with the right role (coder, reviewer, tester), monitor the convoy through to merge via the refinery. Use whenever the user says "have an agent fix X", "swarm on Y", "open PR for Z across all the SDK forks", "assign this work", or anything else that turns a beads task into a running async agent.

Discover MATLAB Parallel Computing Toolbox clusters on the network and in the cloud, and manage their profiles — list, inspect, import, export, set default, validate, and delete. Use whenever the user asks what parallel computing resources, clusters, or cluster profiles they have or can use — e.g. "what parallel resources do I have", "show my cluster profiles", "list clusters", "what clusters can I run on", "where can I submit jobs" — and for any work with parcluster, parallel.listProfiles, parallel.defaultProfile, MJS / Generic / HPC Server / MJSComputeCloud clusters, .mlsettings files, or profile validation. Does NOT cover job submission, parpool, or parfor.

The AlterLab front door and multi-agent launcher — routes a task to the right AlterLab skill(s) when the user invokes the suite without naming one, and for a multi-stage goal (or on the keyword 'alterflow', aliases 'alterresearch' / 'ultralab') it CLARIFIES the goal with a few questions, SELECTS the skills the task needs, and runs a dynamic multi-agent workflow composing them (via alterlab-workflow-orchestration, alterlab-research-pipeline, or alterlab-ssci-orchestrator). Triggers on 'use AlterLab skills', 'which AlterLab skill for X', 'is there an AlterLab skill for…', a multi-stage research goal, 'alterflow …', or any generic AlterLab request where the user does not know skill names. It always asks clarifying questions before executing a multi-step run. Use when someone references AlterLab generically, describes a multi-stage goal, or fires the alterflow keyword; when the user already names a specific skill, defer to that skill directly. Part of the AlterLab Academic Skills suite.

Runs a 13-agent deep research pipeline for rigorous academic work on any topic across 7 modes (full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis), covering research-question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk-of-bias assessment, meta-analysis, APA 7.0 report compilation, editorial and devil's-advocate review, ethics review, and post-research literature monitoring. Use when the request mentions research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, or 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題. Part of the AlterLab Academic Skills suite.