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dsh-conversation-stats

Client Updated 2026.08.20

Run the following command in DeepSeek Harness:

dsh plugin install wellcover/dsh-conversation-stats

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install wellcover/dsh-conversation-stats in a DeepSeek Harness terminal to fetch and install the plugin from https://github.com/wellcover/dsh-conversation-stats, then restart dsh web to see the new Conversation Stats tab.

About this plugin

You run dozens of conversations in DSH every day, yet the dialogue list only shows titles and timestamps. Digging through zstd-compressed logs to find out which session burned the most tokens, how well the cache performed, or which step took the longest is tedious and error-prone. dsh-conversation-stats puts all of that on a single screen: a new Conversation Stats tab appears in the DSH Web GUI, aggregating every historical session key metrics with no extra service or resident process. Data survives restarts as-is.\n\nThe experience centers on an overview table and a per-session detail view. The overview lists each session in reverse chronological order with turns, steps, model-call count, input/output/cache tokens, and cache-hit rate, while summary chips at the top give you an at-a-glance picture of total spend. Clicking any row expands the full breakdown: per-call model details (timestamp, model, finish reason, token split), tool-call statistics (count, total duration, failures), model distribution, and LLM vs. tool latency. Data auto-refreshes every 60 seconds or on demand. The table also includes a Delete column (with a red confirmation dialog) for permanently removing a session directory, and a Load column that restores an archived session back into the DSH dialogue list.\n\nUnder the hood, the plugin reads session.jsonl.zstd files directly from ~/.dsh/sessions, decompresses each zstd frame natively, parses the newline-delimited JSON event stream, and collapses statistics using the same logic as the built-in sessionStats projection. No API server, no daemon. It is well suited for heavy multi-turn users who track token cost and cache efficiency, or for teams that accumulate large backlogs of sessions and need regular cleanup and audit trails.

Screenshots

Use Cases

  • Quickly review token spend, cache hit rate, and model call distribution per session
  • Clean up stale session logs and restore archived conversations back to the list
  • Compare LLM vs. tool latency across sessions to pinpoint slow steps

Best For

  • Heavy multi-turn users who track token cost and cache efficiency
  • Teams that need regular audit and cleanup of session backlogs
  • Individual developers who want to verify session metrics with no extra daemon