Loading...
Loading...
Found 13,656 Skills
Use when adding Bale (بله) support to Hermes Gateway. Connect AI agent to Persian messenger via free Bot API.
Conventions for writing sharp, concise prose and Markdown, for human readers and AI agents. Read before writing or editing documentation, READMEs, instructions, agent skills, or any prose text.
Interactive session to create Instructions field content for the Sanity Context MCP server. Use this skill whenever users mention tuning agent context, improving agent responses to Sanity data, configuring MCP instructions, setting up content filters, or when their agent gives wrong results from Sanity queries. Also trigger when users say their agent is confused about schema relationships, needs data-specific guidance, or wants to optimize which content the agent can access.
(NS) Orchestrate partitioned version implementation slice-by-slice — one subagent per slice, commit, auto-advance until done or stop. Use when version-roadmap.md has pending slices and the user asks to orchestrate/execute all slices. Do NOT use for non-partitioned versions, ad-hoc coding (ns-code-coder), or partitioning itself (ns-sdd-version-partitioner).
Work with the upstash-box Python SDK for sandboxed cloud containers with AI agents, shell, filesystem, git, cron schedules, and a headless browser. Use when building with Upstash Box in Python, creating sandboxed environments, running AI agents in containers, browser automation from a box, or orchestrating parallel boxes.
Creative-writing domain knowledge for durable story state. Load when preserving or retrieving project memory — fact extraction, context scoping, reference writing, artifact layout, and issue tracking. If you are a knowledge agent such as kb-lead, load this for the fiction-specific categories and conventions your general methodology doesn't cover.
Design ObjectStack AI skills, tools, knowledge sources, conversations, model registry entries, and MCP integrations. Use when the user is adding `*.skill.ts` / `*.tool.ts`, configuring an LLM provider, wiring agent tools, or indexing ObjectStack data as a knowledge source for RAG. Agents themselves are platform-internal (`ask` / `build`) — third parties extend them via skills and tools, not by authoring `*.agent.ts`. Do not use for general LLM prompting questions unrelated to ObjectStack metadata.
Use when a developer asks their coding agent to initialize or work with moldea; plan an AI- or agent-enabled system and decide what should be agents versus deterministic software, services, tools, or human control; create or refine an AI agent or its behavioral system, including instructions, descriptions, handoff descriptions, tools, skills, schemas, variables and providers, routing or handoffs, bindings, or runtime integration; evaluate, reconcile, or validate an existing moldea system; or make ordinary behavior-affecting repository changes that may require maintaining an adopted moldea system. Loading the skill does not adopt moldea: initial adoption still requires explicit developer intent, while relevance-triggered maintenance applies once a repository uses or is adopting moldea.
Review a pull request or a set of code changes for bugs, logic errors, and project-convention violations using a confidence-filtered, multi-agent process. Use this skill when the user asks to review a PR, audit pending changes, or inspect a diff for problems before merging.
Use when long-running or parallel agent work must respect 5-hour and weekly usage limits by checking usage between waves, pausing near the cap, and resuming only when the window is clear.
Apply the same orchestration as `/efficient-fable` to any high-cost frontier model: delegate research, coding, and testing to cheaper subagents while keeping planning, synthesis, and final review with the expensive model.
Use when asked to compare, cross-review, merge, judge, choose, or arbitrate competing plans from multiple agents such as Codex and Claude Code; when given two or more proposed plans, session IDs, transcripts, plan documents, PR descriptions, or pasted strategies; or when the user wants one recommended execution plan after agents review each other's proposals.