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zedarvateszedarvates/botte-secrete/skills/dashboard/SKILL.md

dashboard

Skill2 starsChanged 3 months agoMITView on GitHub

Generate one self-contained, timestamped HTML dashboard of the system's cost picture — routing savings (control loop), metric trends, current metrics, and the cost of outstanding fixes. Also renders as a live ANSI terminal view (--tui, --watch) and serves a live HTTP API (api.py). Use when the user wants a single visual view of cost/savings/health over time, or a live terminal view they don't have to open a browser for.

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The file

Files
SKILL.md

Dashboard Skill

Live metrics visualization for botte-secrete — one data source, three views (timestamped HTML report, ANSI TUI, live HTTP API). The live control room also leads with a three-line Quality Compass: state, reason, and next safe action.

Usage

python -m skills.dashboard.cli .              # → .botte/reports/dashboard_<stamp>.html
python -m skills.dashboard.cli . --json        # the assembled data
python -m skills.dashboard.cli . --tui         # same data, rendered as ANSI panels once
python -m skills.dashboard.cli . --watch       # re-render every --interval seconds (default 5s)

Assembles live numbers from control_loop (routing savings), trends (metrics over time), metrics (LOC/cost), and fix (cost to apply outstanding fixes) into one timestamped HTML page, browsable any time. Exposed via [[llm_mcp]] as dashboard. Related: [[control_loop]], [[trends]], [[metrics]], [[fix]], [[report]], [[demo]] (shares the same ANSI panel renderer), [[events]].

--tui / --watch

skills/dashboard/tui.py renders the exact same collect() dict the HTML report uses — one data source, two views. --tui prints one frame; --watch loops, re-collecting and re-rendering every --interval seconds (Ctrl+C to stop) — the metrics/routing/fixes counterpart to demo --live's routing feed. Metric panels include unicode sparklines (▁▂▅▇) sourced from trends.show()'s last 10 snapshots — run python -m skills.trends.cli snapshot . periodically (e.g. from CI) to build up history.

--fleet — every project on this machine, one view

python -m skills.dashboard.cli fleet add /path/to/project    # opt-in registry
python -m skills.dashboard.cli fleet list
python -m skills.dashboard.cli fleet remove /path/to/project
python -m skills.dashboard.cli --fleet [--json]               # aggregate them all

~/.botte/fleet.json is an explicit opt-in registry, not a filesystem scan — nothing gets touched unless you fleet add it. --fleet runs collect() on every registered project and sums LOC / tokens saved / outstanding fixes; a project that's vanished or errors out is reported under errored, not silently dropped or a crash. See skills/dashboard/fleet.py.

Live HTTP API

  • index.html — Dashboard UI
  • api.py — HTTP API (/api/stats, /)
  • cron_hook.py — Periodic notifications
# Start the loopback-only API server
python -m skills.dashboard.api

# View dashboard
open http://127.0.0.1:8765

# CLI stats
python -c "from skills.dashboard.api import load_metrics; print(load_metrics())"

# Build the sanitized GitHub Pages artifact
python scripts/generate_public_dashboard.py --output .botte-cache/public-dashboard

The live server binds to 127.0.0.1 by default and reads local operational metrics. The static generator deliberately excludes local Memory Hub and Decision Ladder data plus Quality Compass task/evidence material; CI can publish only the repository test summary and a local-evidence-private placeholder. The tracked docs/dashboard.html is a launcher and never embeds demo values.

The Quality Compass reads the private verified ledger and, when installed, the bounded Wave 2 outcome envelope contract. Its default view stays non-technical; native <details> disclosure exposes aggregate coverage, abstentions, route comparison, versions, resources, drift, and recent evidence. Every learned signal remains SHADOW, and high-impact outcomes display the human gate.

Metrics served

  • tests_passed / tests_failed — Latest observed scripts/run_tests.py result
  • tests_status / tests_partial / tests_stale — Result provenance and freshness
  • lines_saved — Lines avoided via decision ladder
  • avoidable_pct — % of tasks that didn't need new code
  • by_rung — Breakdown by decision ladder rung
  • compressor — Compression stats
  • memory_entries / memory_projects — Governed Memory Hub aggregate counts
  • memory_by_status / memory_by_asset — Aggregate lifecycle/type counts only
  • legacy_memory_entries — AutoMemory count during the migration window
  • quality_compass — Three-line qualitative state plus local progressive evidence

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