dsh-observability：为 DSH 外部子代理调用补上归属观测
dsh-observability 是 jeremy9682 维护的 MIT 许可插件，旨在解决 DSH 调用外部子代理（如 Codex、Cursor）后无法确认会话产物的可观测性问题。该仓库包含两个核心插件：dsh-codex-observability 和 dsh-tool-cursor。前者通过指纹追踪 `~/.codex/sessions` 等目录，将 `sessionRef` 等归属指针写入 journal 文件，仅记录指针而不存储 prompt 或 transcript 内容；后者提供 `cursor_delegate` 工具，支持将任务委托给 Cursor CLI，默认只读且编辑
Read Moredsh-o11y-plugin：为 deepseek-harness 提供统一插件维度可观测性
dsh-o11y-plugin 是专为 deepseek-harness (dsh) 设计的可观测性插件，旨在统一插件维度的遥测管理。它通过注册全局 OpenTelemetry providers，使基于标准 @opentelemetry/api 的插件能够便捷导出 traces、metrics 和 logs，并将 dsh 会话遥测桥接为 OTel logs。该插件支持 headless 与 web 两种 profile，通过 OTLP/HTTP 端点导出数据；若未配置或端点不可达，数据将被静默丢弃。插件具备无状态特性，移除时无需清理数据，且不写入文件、不存储凭据，遥测数据在导出前仅驻留内存。
Read Moredsh-plugin-langfuse: Exporting DSH Agent Sessions to Langfuse
dsh-plugin-langfuse is a community plugin for DeepSeek Harness maintained by linyp, implementing session telemetry integration. It exports each round of conversation as OpenTelemetry traces compliant with GenAI semantics, writes them into Langfuse via OTLP/HTTP, and supports feedback Scores, session aggregation, and fork lineage. This article introduces its positioning, core capabilities, profile bundle installation steps, environment variables and mode configuration, as well as considerations such as regional keys, privacy controls, and runtime permissions.
Read More# Sentry Skill: Integrating Production Error Troubleshooting into AI Agent Workflows
Sentry is an Agent Skill maintained by OpenAI in the curated directory of the openai/skills repository, which enables read-only observability queries via the official Sentry CLI: listing unresolved issues, viewing event details, and calling issue explain/plan to generate root cause analysis and repair suggestions. This article introduces its positioning, core commands, installation methods for both the CLI and the Skill (Codex's $skill-installer and Cursor's .cursor/skills), as well as precautions including the default 24-hour production environment query scope, JSON output format and security desensitization, which is suitable for DevOps/SRE to integrate online troubleshooting into AI workflows.
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