القائمة
Progressive disclosure pattern for MCP tools and skills. Teaches agents just-in-time discovery to minimize token usage.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/diegosouzapw/awesome-omni-skill --skill progressive-discoveryيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Token-efficient tracking for AI orchestration. CLI-first for status updates (~50 tokens), agent fallback for complex ops (~1KB). Use when: updating task status, querying blockers, creating progress files, validating phases.
AshAi extension guidelines for integrating AI capabilities with Ash Framework. Use when implementing vectorization/embeddings, exposing Ash actions as LLM tools, creating prompt-backed actions, or setting up MCP servers. Covers semantic search, LangChain integration, and structured outputs.
This skill should be used when solving hard questions, complex architectural problems, or debugging issues that benefit from GPT-5 Pro or GPT-5.1 thinking models with large file context. Use when standard Claude analysis needs deeper reasoning or extended context windows.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | progressive-discovery |
| description | Progressive disclosure pattern for MCP tools and skills. Teaches agents just-in-time discovery to minimize token usage. |
| category | core |
| alwaysApply | true |
| injectionFormat | content |
| metadata | {"gobby":{"audience":"all","format_overrides":{"autonomous":"full"}}} |
Skills — context-aware prompts for common workflows.
list_skills() on gobby-skillsget_skill(name="skillname") on gobby-skillssearch_skills(query="topic") on gobby-skillssearch_hub(query) on gobby-skillsMCP Tools — progressive disclosure (each step gates the next):
list_mcp_servers() — discover servers (~50 tokens)list_tools(server_name) — discover tools (~100 tokens per server)get_tool_schema(server_name, tool_name) — get full inputSchema (just-in-time)call_tool(server_name, tool_name, arguments) — executeCore principle: NEVER load all schemas upfront — 50+ tool schemas consume 30-40K tokens. Fetch schemas only when you're about to call a tool. This reduces token usage by ~95%.
# WRONG — Loading everything upfront (wastes 30-40K tokens)
for server in servers:
for tool in list_tools(server):
get_tool_schema(server, tool) # Unnecessary!
# WRONG — Guessing parameters without schema
call_tool("gobby-tasks", "create_task", {"name": "Fix bug"}) # Wrong param!
# RIGHT — Just-in-time discovery
get_tool_schema("gobby-tasks", "create_task") # Learn: needs "title" not "name"
call_tool("gobby-tasks", "create_task", {"title": "Fix bug", "session_id": "#123"})
| Server | Purpose |
|---|---|
gobby-tasks | Task management |
gobby-sessions | Session handoff |
gobby-memory | Persistent memory |
gobby-workflows | Workflow control |
gobby-agents | Agent spawning |
gobby-worktrees | Git worktrees |
gobby-clones | Repository clones |
gobby-merge | Merge resolution |
gobby-hub | Hub / cross-project |
gobby-skills | Skill management |
gobby-metrics | Usage metrics |
gobby-artifacts | Artifact storage |
gobby-pipelines | Pipeline execution |
Use list_mcp_servers() to see all connected servers with live status.