zzhzz/dsh-ask-peer
Peer-to-peer colleague-agent consultation for DSH: ask one or several configured peers, queue asynchronous questions, discover peers through signed friend cards, and review inbound requests in the Web UI.
About this plugin
The core design is peer-to-peer: no broker, no shared database, no company server. Every agent runs its own endpoint and keeps its own copy of its relationships, so the network has no single point of control — asking happens directly between two agents over the LAN. One agent asks another, and gets a committed answer grounded in the colleague's own workspace and session context. The result is a community of agents that discover each other, vouch for each other, and share expertise — with every r
$ dsh plugin --profile web add dsh-ask-peer$ dsh plugin --profile web add github:zzhzz/dsh-ask-peerHealth breakdown
62 / 100Score reflects license, community signals, documentation and distribution. It is not a code audit — review the source before installing.
Security
Key metrics
Related
Related plugins
Runs a local OpenAI-compatible gateway so DSH can use the model quota of a logged-in Tencent CodeBuddy (WorkBuddy) account: one-shot installer with SHA256 verification, logon scheduled task with duplicate-launch guard, manage.ps1 for status/start/stop/restart/logs/update/uninstall, and an agent skill that drives setup and troubleshooting.
dsh-agent-teams turns the current DeepSeek Harness session into a captain that can assemble durable sub-agents, split a goal into dependency-aware tasks, and coordinate work through direct messages.
Native conversational image generation for DeepSeek Harness: ask the agent to create an image, and it handles generation and keeps the result directly in the conversation.
Visible agent browser over CDP: 30+ bcdp_* automation tools, realtime SSE watch panel with direct click/scroll/IME input, element picking quoted into the composer as balanced @"picks/*.json" references, and human-verification notices.
Per-round thinking-level (reasoning_effort) control for DeepSeek Harness: pick Auto and it schedules low/high/max from the recent tool-call history, or fix a wire level (off/on/minimal/low/medium/high/xhigh/max) manually, with custom wire mapping, a model-aware capability guard, and context-window presets (64K–1M); its per-model capability card pairs with a separate plugin, [dsh-llm-openai-completions](https://github.com/drscrewdriver/dsh-llm-openai-completions) (installable from dsh-market), so custom OpenAI-compatible gateways (vLLM / LM Studio / self-hosted) can try reasoning — the card writes llm-pi-ai capabilities, the adapter drives the wire (compat.thinkingFormat), and this plugin auto-maintains the takeover list.
DeepSeek Harness bundle registering one skill that turns long-form production (academic papers, industry analysis, business commentary) into a 9-role pipeline: T1 literature / T2 data / T3 case scouts running in parallel, T4 analyst, T5 writer, T6 critical companion, T7 auditor, T8 finalizer run by the coordinator, T9 peer reviewer — across 6 phases with triangular evidence verification, a 26-check M-gate (25 scripted checks plus one human checkpoint), a G0-G16 audit including a Chinese AI-trace gate, peer-review scoring with journal matching, and 4 human checkpoints. Installs with `dsh plugin add lunheng-article-pipeline` (package.json#dsh.bundle.patch plus a lib/index.js entry that registers the skill); ships role cards for T1-T9, templates and 36 zero-dependency verification scripts, with five-language READMEs.