Turn Scattered Diagnosis Snapshots into Deliverable Reports

Where this fits

You've been running a multi-day diagnosis with an AI agent, saving snapshots after each session via dbs-save. Now your client asks for 'something to show the partner' — but the conclusions are scattered across chat history. The dbs-report skill solves this by merging all accumulated snapshots into a single, timestamped markdown report. It's the difference between a tool that produces fragments and one that produces a deliverable.

Why agents benefit

• Reads only confirmed state: Instead of re-summarizing from conversation, it consolidates ~/.dbs/sessions/{project}/*.md files — data the user already validated. This preserves trustworthiness.
• Orders, dedupes, and categorizes: Time-ordered merging with deduplication means no repeated findings and a logical flow from diagnosis to recommendation.
• Filters by date or project: Handles --since and --slug flags, so you can generate a focused report for a specific week or subproject without noise.
• User-friendly language: Internal terms like 'snapshot' become '存档', and frontmatter stays hidden — the report reads like a consultant wrote it, not an API.
• Trigger via natural language: Users can say '打包' or 'make me a report' — no need to remember a command.

Practical scenarios

Preparing a client handoff

After three diagnosis sessions for a legacy codebase, the user types /dbs-report. The agent produces a single markdown file with a table of contents, all confirmed findings, and a version history — ready to email to the external consultant.

Weekly progress review for stakeholders

Using /dbs-report --since 2025-01-01, the agent filters out older snapshots and creates a progress-only report. This gives the partner a clean view of what changed in the last month, without manual copy-paste.

Cross-project comparison for portfolio decisions

The agent runs /dbs-report --slug projectA and --slug projectB, then merges the two reports into one comparative document. Because each report is deterministic, the comparison is apples-to-apples.

Add it to your agent workflow

Install with:

npx skills add Pluviobyte/rnskill --skill dbs-report

Example usage in an agent conversation:

User: 整理一份给合伙人看的
Agent: Reading snapshots from ~/.dbs/sessions/my-project/ …
       Found 5 archives, merging into report-2025-03-01.md

The generated markdown includes datetime, version index, and a status summary per finding.

Compare before adopting

Before committing, evaluate:

• Quality signals: Check the GitHub repo (1381 stars) for open issues about merge conflicts or duplicate detection. Are edge cases handled?
• Maintenance freshness: Look at the last commit date and whether the author (Pluviobyte) responds to PRs. A stale skill may break with newer agent runtimes.
• Alternatives: Consider whether your agent already has a built-in report aggregator, or if a templating script would serve you better. dbs-report is opinionated about folder structure.
• Workflow fit: Does your team already use dbs-save? If not, this skill only helps after you adopt that pattern.

Why it is worth tracking

The steady star count and clear README indicate a niche but well-maintained tool. It solves a recurring pain point for agent-assisted diagnosis — turning ephemeral chat into an audit trail. Worth re-evaluating when you start running multi-session consulting workflows or when client-facing deliverables become routine. For more skill comparisons, check out OpenAgentSkill — where this skill is indexed alongside similar automation utilities.