session-report
This skill analyzes Claude Code session transcripts and generates an interactive HTML report, visualizing token usage, cache hit rates, subagent activity, skill distribution, and expensive prompts, enabling users to quickly identify anomalies, optimize resource allocation, and boost AI development efficiency.
npx skills add https://github.com/anthropics/claude-plugins-official --skill session-reportBefore / After Comparison
1 组Users manually extract and analyze token, cache, and subagent data from Claude Code logs, which takes an average of 2 hours and is prone to missing key metrics.
This skill automatically collects data and generates a visual HTML report, presenting all key metrics in one click, taking only 10 minutes, with interactive exploration.
Session Report
Produce a self-contained HTML report of Claude Code usage and save it to the current working directory.
Steps
-
Get data. Run the bundled analyzer (default window: last 7 days; honor a different range if the user passed one, e.g.
24h,30d, orall). The scriptanalyze-sessions.mjslives in the same directory as this SKILL.md — use its absolute path:node <skill-dir>/analyze-sessions.mjs --json --since 7d > /tmp/session-report.jsonFor all-time, omit
--since. -
Read
/tmp/session-report.json. Skimoverall,by_project,by_subagent_type,by_skill,cache_breaks,top_prompts. -
Copy the template (also bundled alongside this SKILL.md) to the output path in the current working directory:
cp <skill-dir>/template.html ./session-report-$(date +%Y%m%d-%H%M).html -
Edit the output file (use Edit, not Write — preserve the template's JS/CSS):
- Replace the contents of
<script id="report-data" type="application/json">with the full JSON from step 1. The page's JS renders the hero total, all tables, bars, and drill-downs from this blob automatically. - Fill the
<!-- AGENT: anomalies -->block with 3–5 one-line findings. Express figures as a % of total tokens wherever possible (total =overall.input_tokens.total + overall.output_tokens). One line per finding, exact markup:
Classes:<div class="take bad"><div class="fig">41.2%</div><div class="txt"><b>cc-monitor</b> consumed 41% of the week across just 3 sessions</div></div>.take badfor waste/anomalies (red),.take goodfor healthy signals (green),.take infofor neutral facts (blue). The.figis one short number (a %, a count, or a multiplier like12×). The.txtis one plain-English sentence naming the project/skill/prompt; wrap the subject in<b>. Look for: a project or skill eating a disproportionate share, cache-hit <85%, a single prompt >2% of total, subagent types averaging >1M tokens/call, cache breaks clustering. - Fill the
<!-- AGENT: optimizations -->block (at the bottom of the page) with 1–4<div class="callout">suggestions tied to specific rows (e.g. "/weekly-statusspawned 7 subagents for 8.1% of total — scope it to fewer parallel agents"). - Do not restructure existing sections.
- Replace the contents of
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Report the saved file path to the user. Do not open it or render it.
Notes
- The template is the source of interactivity (sorting, expand/collapse, block-char bars). Your job is data + narrative, not markup.
- Keep commentary terse and specific — reference actual project names, numbers, timestamps from the JSON.
top_promptsalready includes subagent tokens and rolls task-notification continuations into the originating prompt.- If the JSON is >2MB, trim
top_promptsto 100 entries andcache_breaksto 100 before embedding (they should already be capped).
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