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Use when the user wants to extract arXiv IDs or DOIs from research or RSS digests and turn them into getscipapers requests or manifests.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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.