菜单
Use when the user wants to extract arXiv IDs or DOIs from research or RSS digests and turn them into getscipapers requests or manifests.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Single-agent paper review workflow.
Comprehensive guide and reference for the Antigravity Customization System. Use to explain how customizations work, their loading priority, discovery mechanisms, and to guide the creation of skills, rules, plugins, hooks, and MCP servers.
Provides a comprehensive guide, quick reference, and sitemap for Google Antigravity (AGY), including the Antigravity CLI (agy), Antigravity 2.0, Antigravity IDE, Python SDK, slash commands, keybindings, and customizations (skills, rules, MCP, sidecars). Activate this skill when the user asks questions about how to use, configure, or customize Antigravity, AGY, the agy CLI, the Antigravity IDE, or Antigravity 2.0.
Guidelines for interacting with GitHub and request permissions from the user when commands fail due to restrictions in the agent environment.
ALWAYS use this skill when the user asks to send, get, retrieve, find, share, add, or search for a paper. This skill manages the user's Zotero library with 10,000+ papers and can retrieve PDFs, create share links, add new papers, and search. Prefer this over getscipapers for any request involving sending/getting/finding papers.
Use when the user asks to send, get, retrieve, find, share, add, or search for a paper. This is the live OpenClaw Zotero workflow adapted for Codex and should take priority over external paper retrieval.
| name | digest-bridge |
| description | Use when the user wants to extract arXiv IDs or DOIs from research or RSS digests and turn them into getscipapers requests or manifests. |
| metadata | {"short-description":"Bridge digest outputs into paper retrieval"} |
This skill is installed as an OpenCode-native SKILL.md. For runtime-backed
helpers, prefer the shared ai-agents-skills runtime root and the
AAS_RUNTIME_ROOT override instead of assuming a Codex-specific runtime
path.
On native Windows, use the managed Windows runner and the native runtime command target. Set $runtime to the installed runtime root. Multi-agent installs usually use %LOCALAPPDATA%\ai-agents-skills\runtime. Then run:
$runtime = if ($env:AAS_RUNTIME_ROOT) { $env:AAS_RUNTIME_ROOT } else { "$env:LOCALAPPDATA\ai-agents-skills\runtime" }
& "$runtime\run_skill.bat" "skills/digest-bridge/run_digest_bridge.bat" <args>
POSIX examples below use run_skill.sh and .sh command targets; use the Windows command target above on native Windows.
This uses the managed ai-agents-skills runtime copy of the digest bridge workflow.
$AAS_RUNTIME_WORKSPACE/skills/digest-bridge/This is a direct Python entry point, so run it from the managed ai-agents-skills runtime workspace with the workspace-local PYTHONPATH.
Use functions.exec_command.
cd "$AAS_RUNTIME_WORKSPACE" && PYTHONPATH="$AAS_RUNTIME_WORKSPACE/.local:${PYTHONPATH:-}" python3 skills/digest-bridge/digest_bridge.py scan
cd "$AAS_RUNTIME_WORKSPACE" && PYTHONPATH="$AAS_RUNTIME_WORKSPACE/.local:${PYTHONPATH:-}" python3 skills/digest-bridge/digest_bridge.py scan --source research --min-score 3
cd "$AAS_RUNTIME_WORKSPACE" && PYTHONPATH="$AAS_RUNTIME_WORKSPACE/.local:${PYTHONPATH:-}" python3 skills/digest-bridge/digest_bridge.py request --source research
cd "$AAS_RUNTIME_WORKSPACE" && PYTHONPATH="$AAS_RUNTIME_WORKSPACE/.local:${PYTHONPATH:-}" python3 skills/digest-bridge/digest_bridge.py request --source rss --watch
--source and --min-score filters instead of broad requests when the user wants a narrower batch.getscipapers-requester.scan is the dry-run discovery step; request is the transition into manifest/watch creation.