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
Use when the user asks for a multi-agent discussion, panel review, multi-agent review, or multi-agent research session with role selection, round control, and template-based orchestration.
Use when drafting, validating, or normalizing bounded cross-agent task/result packets for parent-controlled handoffs. This is a packet-contract skill, not a runtime delegation broker.
Send email over SMTP using only the Python standard library, with plain-text and HTML bodies, file attachments, cc/bcc, reply-to, a dry-run preview, connection verification, and redacted config inspection.
Runtime helper for autonomous-research-loop ledgers. Use to initialize, append, validate, inspect, or smoke-test autonomous research loop state files without network, package installation, provider CLI calls, or live agent spawning.
Run bounded autonomous research iterations with evidence gates, recovery ledgers, and optional cross-agent handoffs. Use when the user asks to continue research autonomously, run a research loop, integrate autonomous agent loops, or keep improving a research workflow without repeated prompts.
Use for external DOI/ISBN/title resolution, manifest creation from pasted text, and paper retrieval after the local library-first workflow does not satisfy the request or the user explicitly opts out of library use.
| 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"} |
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.