From sift
Sift through AI coding assistant usage across platforms — cost, efficiency, cache, productivity, and session health metrics with actionable insights
How this skill is triggered — by the user, by Claude, or both
Slash command
/sift:usage-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Sift through usage data across all AI coding tools (Claude Code, Copilot CLI, VS Code Copilot, Cursor, Gemini CLI, Codex CLI). Produces metrics, reports, a dashboard, and a machine-readable JSON export.
Sift through usage data across all AI coding tools (Claude Code, Copilot CLI, VS Code Copilot, Cursor, Gemini CLI, Codex CLI). Produces metrics, reports, a dashboard, and a machine-readable JSON export.
The binary is bundled as a zipapp. All commands use ${CLAUDE_PLUGIN_ROOT}:
# Full analysis (all time, all sources)
python3 ${CLAUDE_PLUGIN_ROOT}/skills/usage-analysis/analyzer.pyz
# Scoped analysis
python3 ${CLAUDE_PLUGIN_ROOT}/skills/usage-analysis/analyzer.pyz --days 7
python3 ${CLAUDE_PLUGIN_ROOT}/skills/usage-analysis/analyzer.pyz --days 30 --source claude-code
python3 ${CLAUDE_PLUGIN_ROOT}/skills/usage-analysis/analyzer.pyz --project my-repo
# Discover available sources and projects
python3 ${CLAUDE_PLUGIN_ROOT}/skills/usage-analysis/analyzer.pyz --list
Always start with --list if the user hasn't specified a scope, to understand what data is available.
All outputs go to ~/.sift/:
| File | Purpose |
|---|---|
export.json | Primary artifact — machine-readable, all metrics + per-session data |
report.md | Human-readable markdown report |
dashboard.html | Interactive HTML dashboard (open in browser) |
prompts/ | User prompts grouped by project |
After running the analyzer, read ~/.sift/export.json for programmatic analysis. Structure:
{
_schema: "sift-export-v1"
summary: { total_sessions, total_cost_usd, total_tokens, total_tool_calls, projects }
metrics: {
<key>: { title, order, data: { ... } }
}
sessions: [
{ session_id, source, project, date, model, cost_usd, tokens: {...}, activity: {...}, extras: {...} }
]
}
| Metric Key | What It Tells You |
|---|---|
cost_efficiency | Total spend, cost per session/action/output token |
session_outcome | Heuristic success vs failure rate, wasted cost |
retry_ratio | Token waste from debug loops and retries |
model_routing | Savings available from routing light tasks to cheaper models |
cache_efficiency | Cache hit rate, amortization, savings from caching |
session_health | Median/P90/max session sizes, bloat index |
duration_trend | Are sessions getting longer over time? |
prompt_length | Under/over-specification patterns in user prompts |
daily_burn | Daily cost trend with 7-day rolling average |
platform_comparison | Per-source cost and session breakdown |
When presenting findings to the user, focus on:
platform_comparison, model_mix, top_sessions.session_outcome.failure), retry-heavy sessions (retry_ratio), bloated sessions (session_health.sessions_over_50m).model_routing.potential_savings), cache improvement (cache_efficiency), compaction events (child_metrics.compaction_count).daily_burn)? Are sessions getting longer (duration_trend)? Is adoption increasing (project_adoption)?prompt_length).Offer the dashboard (open ~/.sift/dashboard.html) for interactive exploration.
pyyaml (for Copilot CLI source parsing)Provides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Creates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.
npx claudepluginhub andurilcode/sift --plugin sift