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.
60 other projects use a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- arklanq/agent-skillstheir own version, first seen Nov 8, 2026
- charles-adedotun/sre-plugintheir own version, first seen Nov 8, 2026
- paulogabriel/beryltheir own version, first seen Nov 8, 2026
- zedarvates/frugal-skillsedited, first seen Nov 8, 2026
- whoamnesia/astudio-aiotheir own version, first seen Nov 8, 2026
- onelastcommit/noctratheir own version, first seen Nov 8, 2026
Used with
Projects with it also use activity, telemetry and 9 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
gokite-aiSkillactivity
View transaction history and recent account activity. Invoke when the user asks about past spending, wants to verify a payment went through, check if an order completed, review wallet transfers, or see a log of all account actions. Covers wallet transfers, faucet drops, API payments, shopping checkouts, agent registrations, and session approvals.
Also in 3 projects that use this
OpenAISkilltelemetry
Add and verify lightweight macOS runtime telemetry. Use when wiring Logger events or inspecting logs for windows, sidebars, menus, and actions.
Also in 3 projects that use this
anombyte93Skilldiscover
Phase 1 of the prd-taskmaster pipeline: brainstorm-driven discovery. Delegates to superpowers:brainstorming in Interactive Mode (one adaptive question at a time), or self-brainstorms in Autonomous Mode when no user is present. Intercepts before the brainstorming chain hands off to writing-plans — this skill owns the exit. Extracts constraints, calibrates scale (Solo / Team / Enterprise), and advances the pipeline to GENERATE.
Also in 7 projects that use this
soilmassSkillexport
Animation export configurations including format selection, resolution, framerate, quality, and optimization settings. Use when rendering animations for web, mobile, video, or social media. Provides tokens for all export parameters across Lottie, GIF, video, and sprite sheet formats.
Also in 4 projects that use this
seongjaeryuSkillsave
Complete and save your current thinking to Bindly
Also in 4 projects that use this
seongjaeryuSkillorganize
Organize knowledge into Sets
Also in 3 projects that use this
Other versions60 projects use a version of this.
Word for word · 0
None found.
Edited · 1
- zedarvates/frugal-skills★ 165% same
Wrote their own version · 59
- dlt-hub/dlt★ 6.1ksame name
- Zafer-Liu/Data-Analysis-Agent★ 2.6ksame name
- PrimeIntellect-ai/prime-rl★ 2.2ksame name
- suboss87/FDEOps★ 940same name
- SethGammon/Citadel★ 927same name
- egregore-labs/egregore★ 293same name
- hsantanna88/clo-author★ 262same name
- hugosantanna/clo-author★ 262same name
- wellwelwel/lagune★ 177same name
- Aster110/mycc★ 165same name
49 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository75 other files it gives its agents.
- AGENTS.mdAGENTS.md
- clean-cacheSkill
- agent-cacheSkill
- app_testSkill
- asset-qualitySkill
- auto_distillSkill
- auto-memorySkill
- auto_routerSkill
- benchSkill
- bootstrapSkill
- botte-proxySkill
- cacheSkill
- capabilitiesSkill
- cardinalSkill
- cavemanSkill
- checkupSkill
- clarificationSkill
- clusterSkill
- code_fingerprintSkill
- completion_proofSkill
- conductorSkill
- context_budgetSkill
- context_profilerSkill
- context_slicerSkill
- context_windowsSkill
- control_loopSkill
- cost_estimatorSkill
- cwe_kbSkill
- dag_optimizerSkill
- decision-ladderSkill
- demoSkill
- diff_languageSkill
- directives_auditSkill
- docgenSkill
- docs_stewardSkill
- eventsSkill
- fallow_likeSkill
- fast_contextSkill
- fixSkill
- harness_deltaSkill
- hermes-bridgeSkill
- hf_provenanceSkill
- infra_advisorSkill
- ingestSkill
- llm_backendsSkill
- llm_mcpSkill
- loaderSkill
- mcp_gatewaySkill
- meta_harnessSkill
- metricsSkill
- migration_auditSkill
- monte_cristoSkill
- mousquetairesSkill
- nlp_deterministicSkill
- nn_auditSkill
- prefix-prunerSkill
- prefix_treeSkill
- preflightSkill
- prompt_improverSkill
- reportSkill
- The first 60 shown.
The file
Files
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 UIapi.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-dashboardThe 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 observedscripts/run_tests.pyresulttests_status/tests_partial/tests_stale— Result provenance and freshnesslines_saved— Lines avoided via decision ladderavoidable_pct— % of tasks that didn't need new codeby_rung— Breakdown by decision ladder rungcompressor— Compression statsmemory_entries/memory_projects— Governed Memory Hub aggregate countsmemory_by_status/memory_by_asset— Aggregate lifecycle/type counts onlylegacy_memory_entries— AutoMemory count during the migration windowquality_compass— Three-line qualitative state plus local progressive evidence
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

