fft-demo-holodeck-ops
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- Author repo nano-core
- Domain
- AI
- Compatible agents
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- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @0-CYBERDYNE-SYSTEMS-0 · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- macOS · Linux · Windows
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: fft-demo-holodeck-ops
description: Drive the FFT_demo_dash holodeck system — scene triggers, agent commentary, canvas presets, and…
category: ai
runtime: no special runtime
---
# fft-demo-holodeck-ops output preview
## PART A: Task fit
- Use case: Drive the FFT_demo_dash holodeck system — scene triggers, agent commentary, canvas presets, and live weather mirroring — to produce impactful, context-aware dashboard demos during Telegram interactions. Use this skill whenever you respond to a farm query and the FFTdemodash dashboard is live. Your Telegram reply and the dashboard should update together — ….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to use this skill / When not to use this skill / Guardrails” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Drive the FFT_demo_dash holodeck system — scene triggers, agent commentary, canvas presets, and live weather mirroring — to produce impactful, context-aware dashboard demos during Telegram interactions. Use this skill whenever you respond to a farm query and the FFTdemodash dashboard is live. Your Telegram reply and the dashboard should update together — …”.
- **02** When the source has headings, the agent prioritizes “When to use this skill / When not to use this skill / Guardrails” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source mentions slash commands such as `/workspace`; use them first when your agent supports command triggers.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “When to use this skill / When not to use this skill / Guardrails”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: fft-demo-holodeck-ops
description: Drive the FFT_demo_dash holodeck system — scene triggers, agent commentary, canvas presets, and…
category: ai
source: 0-CYBERDYNE-SYSTEMS-0/nano-core
---
# fft-demo-holodeck-ops
## When to use
- Drive the FFT_demo_dash holodeck system — scene triggers, agent commentary, canvas presets, and live weather mirroring…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to use this skill / When not to use this skill / Guardrails” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "fft-demo-holodeck-ops" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to use this skill / When not to use this skill / Guardrails
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} FFT Demo Holodeck Ops
Use this skill whenever you respond to a farm query and the FFT_demo_dash dashboard is live. Your Telegram reply and the dashboard should update together — the audience sees both simultaneously.
When to use this skill
- Any time you answer a farm question and the dashboard is running at
http://localhost:8123. - When a user asks about conditions, alerts, status, or operational decisions.
- When a scene change would make the answer more visually compelling.
- When showing the dashboard to investors, growers, compliance officers, or press.
When not to use this skill
- Do not use for production HA deployments with real physical equipment.
- Do not trigger scenes without reading current farm state first.
Guardrails
- Never run destructive git commands unless explicitly requested.
- Preserve unrelated worktree changes.
- Keep write operations main-chat-only.
- Always read
current.jsonbefore setting scene entities.
Rule: Always Write Agent Commentary
On every response to a farm query, write two entities before sending your Telegram reply:
1. Agent status (short mode label, ≤128 chars):
{
"type": "farm_action",
"action": "ha_set_entity",
"params": {
"entityId": "input_text.agent_status",
"value": "Storm Response Active"
},
"requestId": "act_<ts>_status"
}
2. Agent commentary (your full reasoning/answer, ≤500 chars):
{
"type": "farm_action",
"action": "ha_set_entity",
"params": {
"entityId": "input_text.agent_commentary",
"value": "NWS shows 68mph gusts arriving in ~3h. Pre-charging battery to 95%. Securing greenhouse vents. Activating storm drainage pumps."
},
"requestId": "act_<ts>_commentary"
}
The commentary card on the dashboard renders these live with an animated border. The audience sees the agent reasoning in real time.
Scene System
Trigger scenes by setting input_select.demo_scene via ha_call_service:
{
"type": "farm_action",
"action": "ha_call_service",
"params": {
"domain": "input_select",
"service": "select_option",
"data": {
"entity_id": "input_select.demo_scene",
"option": "Golden Hour Harvest"
}
},
"requestId": "act_<ts>_scene"
}
Available Scenes
| Scene Name | When to Use |
|---|---|
Golden Hour Harvest |
User asks about yields, harvest timing, solar peak, profitable operations |
Morning Irrigation Pulse |
User asks about irrigation, water management, dawn operations |
Midnight Ops |
User asks about night security, overnight battery drain, off-hours ops |
IPM Zone Lockdown |
User mentions pests, spray schedules, quarantine, Zone B issues |
Cannabis Full Bloom |
User asks about grow room status, flower stage, environment setpoints |
Fertigation Drift Crisis |
User asks about EC/pH, nutrient dosing, runoff, feed issues |
Emergency Cascade |
User mentions storm + security together, asks about emergency protocols |
Drone Surveillance Sweep |
User asks about security sweep, perimeter check, anomaly detection |
VIP Tour |
User mentions investors, tour, showing the farm, "make it look good" |
Compliance Audit Crunch |
User mentions METRC, audits, state inspection, compliance scores |
Auto (Weather-Driven) |
Reset to live weather mode after a demo scene |
Each scene: updates theme, 8–20 entity values, writes its own agent_status/commentary automatically.
Canvas Presets
After triggering a scene, optionally load a matching canvas spec for the Agent Canvas view.
Canvas spec files are at /workspace/dashboard/canvas-specs/ (or the HA www path). Load via:
{
"type": "farm_action",
"action": "ha_canvas_set_spec",
"params": {
"spec": { "title": "...", "panels": [ ... ] }
},
"requestId": "act_<ts>_canvas"
}
Or read an existing preset first with ha_canvas_get_spec, then patch.
Live Weather Mirroring
The simulator is running live NWS weather for Cedar Creek TX (30.08, -97.49) by default.
To mirror a different city's live weather into a specific zone, use a Telegram-prompted bash call from the host (not IPC) — or tell the user:
npm run demo:mirror -- --location nyc --zone greenhouse_a
Available locations: cedar-creek, napa-valley, nyc, nyc-greenhouse, seattle, miami, denver, phoenix, florida-citrus
Available zones: greenhouse_a, greenhouse_b, greenhouse_c, outdoor_north, cannabis
Demo Flow: Farmer Ask → Dashboard Response
Every interaction should follow this sequence:
- Read
/workspace/farm-state/current.json— get live entity values and suggested theme - Decide which scene (if any) matches the query context
- Write
agent_status(mode label) - Write
agent_commentary(your answer in ≤500 chars) - Trigger scene if appropriate (via
input_select.demo_scene) - Reply via Telegram — same content as
agent_commentary, expanded if needed
The dashboard and Telegram reply should tell the same story simultaneously.
Example Interactions
"Storm coming tomorrow — what should I prepare?"
- Scene:
Emergency Cascade - Status:
Storm Prep Active - Commentary:
NWS advisory confirms 65mph gusts, 2.8" rain expected. Pre-charging battery to 95%. Securing vent actuators. Activating storm drainage. Harvest window closes in ~6h.
"I'm showing investors in 10 minutes"
- Scene:
VIP Tour - Status:
VIP Mode — All Systems Optimal - Commentary:
Solar at 380kW, battery 95%, all compliance at 98%+. Zero active alerts. Yields tracking 12% above projection. Farm is ready for inspection.
"Zone B IPM alert just came in"
- Scene:
IPM Zone Lockdown - Status:
IPM Protocol — Zone B Quarantine - Commentary:
Pest pressure Zone B: 7.5/10. Room B HVAC isolated to prevent spray drift. Drone launched for survey sweep. Azadirachtin queued. Days since last spray reset.
"How's the grow room doing right now?"
- Read current entities → if nominal, set
Cannabis Full Bloom - Status:
Grow Rooms Nominal — All Rooms Flowering - Commentary:
PPFD 920, DLI 48, air temp 78°F, RH 52%, CO₂ 1200ppm, EC 2.4, pH 6.0. All rooms in flower at optimal setpoints. Expected yield on track.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review