Keep a short, persistent work journal so an agent can resume across sessions. Record completed work, current status, next steps, and unsuccessful attempts, then read the latest entry when restarting or summarize the work when closing it.
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Write, append, or read desk journal entries. The journal is persistent memory — what survives session boundaries. A good entry has: what was done, current state, next step.
安装前可以查看交给 AI 代理的指令内容。
Manage a desk's journal — the persistent memory that survives session boundaries.
Append to desks/<desk-name>/journal.md. Each entry is a section:
## <date> — <short summary>
- **Worked on:** <what was done this session>
- **Current state:** <where things stand right now>
- **Next step:** <what the next session should pick up>
When a desk is being wound down (not just a session ending, but the desk itself closing):
## <date> — Desk closed
- **Summary:** <what this desk accomplished overall>
- **Artifacts:** <what's on the bench from this desk>
- **Handoff:** <anything another desk or the operator needs to know>
At session start, read the desk's journal to pick up context. The most recent entry is the most important — it has the current state and next step. Earlier entries provide history if needed.
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Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
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Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
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Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
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Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
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Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.
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