session-report

name session-report description Generate an explorable HTML report of Claude Code session usage (tokens, cache, subagents, skills, expensive prompts) from ~/.claude/projects transcripts. 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 , or all ). The script analyze-sessions.mjs lives in the same directory as this SKILL.md — use its absolute path: node <skill-dir>/analyze-sessions.mjs --json --since 7d > /tmp/session-report.json For all-time, omit --since . Read /tmp/session-report.json . Skim overall , 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: < 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 > Classes: .take bad for waste/anomalies (red), .take good for healthy signals (green), .take info for neutral facts (blue). The .fig is one short number (a %, a count, or a multiplier like 12× ). The .txt is 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-status spawned 7 subagents for 8.1% of total — scope it to fewer parallel agents"). Do not restructure existing sections. 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_prompts already includes subagent tokens and rolls task-notification continuations into the originating prompt. If the JSON is >2MB, trim top_prompts to 100 entries and cache_breaks to 100 before embedding (they should already be capped).