Iot Fleet
ruvnet/ruflo
Create and manage Cognitum Seed device fleets with firmware policies
Control Tesla vehicles through the official Tesla Fleet API: lock/unlock, remote start, charge control, preconditioning, honk, trunk.
$ npx skills add Anil-matcha/awesome-muse-connectors --skill tesla-fleet-api -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors tesla-fleet-api --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .claude/skills && cp -r skills-src/connectors/tesla-fleet-api .claude/skills/tesla-fleet-api && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "tesla-fleet-api" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/tesla-fleet-api into .claude/skills/tesla-fleet-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tesla-fleet-api", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/tesla-fleet-apiType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Anil-matcha/awesome-muse-connectors --skill tesla-fleet-api -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors tesla-fleet-api --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .agents/skills && cp -r skills-src/connectors/tesla-fleet-api .agents/skills/tesla-fleet-api && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tesla-fleet-api" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/tesla-fleet-api into .agents/skills/tesla-fleet-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tesla-fleet-api", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Anil-matcha/awesome-muse-connectors --skill tesla-fleet-api -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors tesla-fleet-api --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/connectors/tesla-fleet-api .cursor/skills/tesla-fleet-api && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "tesla-fleet-api" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/tesla-fleet-api into .cursor/skills/tesla-fleet-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tesla-fleet-api", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Anil-matcha/awesome-muse-connectors.git --path connectors/tesla-fleet-api--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Anil-matcha/awesome-muse-connectors --skill tesla-fleet-api -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors tesla-fleet-api --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/connectors/tesla-fleet-api .gemini/skills/tesla-fleet-api && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "tesla-fleet-api" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/tesla-fleet-api into .gemini/skills/tesla-fleet-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tesla-fleet-api", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Anil-matcha/awesome-muse-connectors tesla-fleet-apiInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Anil-matcha/awesome-muse-connectors --skill tesla-fleet-api -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .github/skills && cp -r skills-src/connectors/tesla-fleet-api .github/skills/tesla-fleet-api && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "tesla-fleet-api" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/tesla-fleet-api into .github/skills/tesla-fleet-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tesla-fleet-api", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Anil-matcha/awesome-muse-connectors --skill tesla-fleet-api -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Anil-matcha/awesome-muse-connectors tesla-fleet-api --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/connectors/tesla-fleet-api .opencode/skills/tesla-fleet-api && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "tesla-fleet-api" agent skill from https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/tesla-fleet-api into .opencode/skills/tesla-fleet-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tesla-fleet-api", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
tesla-fleet-apiControl Tesla vehicles through the official Tesla Fleet API: lock/unlock, remote start, charge control, preconditioning, honk, trunk.
Tesla Fleet API is an agent skill from Anil-matcha/awesome-muse-connectors. Control Tesla vehicles through the official Tesla Fleet API: lock/unlock, remote start, charge control, preconditioning, honk, trunk. Trigger phrases: tesla car, tesla vehicle, lock my tesla, precondition tesla, tesla charge.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `bin/tesla-fleet-api.py`).
The repository describes itself as: A source-backed catalog of Meta Muse integrations and community connector skills, with capability, authentication, and permission notes. The licence is MIT.
6 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d6dc5d8. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Tesla Fleet API loads about 1.7k tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 549 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from Anil-matcha/awesome-muse-connectors at commit d6dc5d8, republished under its MIT licence (© Anil-matcha). 549 words, ~1,699 tokens.
.claude/skills/tesla-fleet-api/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Control Tesla vehicles through the official Tesla Fleet API: read live vehicle state, wake a sleeping car, and send signed commands (lock/unlock, keyless drive, charge control, preconditioning, honk/flash, trunk, sentry/valet, speed limit, navigation). Use when the user mentions their Tesla car or asks for vehicle actuation.
This connector covers vehicles only and is fully separate from tesla-powerwall (energy devices): separate credential, separate scope set, separate CLI.
All commands go through bin/tesla-fleet-api.py. --region selects the API region (na, eu, cn; default na). --vehicle is the vehicle tag, usually the VIN.
bin/tesla-fleet-api.py auth # verify the OAuth token
bin/tesla-fleet-api.py vehicles # list vehicles
bin/tesla-fleet-api.py vehicle-data --vehicle YOUR_VIN # live state (charge, location, climate, doors)
bin/tesla-fleet-api.py vehicle-data --vehicle YOUR_VIN --endpoints charge_state
bin/tesla-fleet-api.py wake --vehicle YOUR_VIN # wake a sleeping car (metered)
# HIGH actuations: --confirm "<exact effect>" required on EVERY call
bin/tesla-fleet-api.py command --vehicle YOUR_VIN --action door_lock \
--confirm "lock all vehicle doors"
bin/tesla-fleet-api.py command --vehicle YOUR_VIN --action door_unlock \
--confirm "unlock all vehicle doors"
bin/tesla-fleet-api.py command --vehicle YOUR_VIN --action remote_start_drive \
--confirm "enable keyless driving for 2 minutes"
# MEDIUM actuations: --confirm "<exact effect>" on first use per vehicle, then proceed
bin/tesla-fleet-api.py command --vehicle YOUR_VIN --action charge_start \
--confirm "start EV charging"
bin/tesla-fleet-api.py command --vehicle YOUR_VIN --action set_charge_limit \
--params '{"percent": 80}' --confirm "change the charge limit"
# LOW actuations: no confirmation
bin/tesla-fleet-api.py command --vehicle YOUR_VIN --action honk_horn
bin/tesla-fleet-api.py command --vehicle YOUR_VIN --action auto_conditioning_start
bin/tesla-fleet-api.py command --vehicle YOUR_VIN --action actuate_trunk --params '{"which_trunk": "rear"}'Available actions: door_lock, door_unlock, remote_start_drive, charge_start, charge_stop, set_charge_limit, honk_horn, flash_lights, auto_conditioning_start, auto_conditioning_stop, actuate_trunk, set_sentry_mode, set_valet_mode, speed_limit_activate, speed_limit_set_limit, navigation_gps_request, schedule_software_update. Extra command parameters go in --params as a JSON object.
MEDIUM first-use confirmations are recorded locally (~/.cache/muse-connectors/tesla-fleet-api/confirmed.json); HIGH actions always ask.
tesla-fleet-api (credential is collected as custom.tesla-fleet-api)credentials.request_api_access); same OAuth pattern as the slack and x connectorsopenid, offline_access, vehicle_device_data, vehicle_cmds, vehicle_charging_cmds, vehicle_locationfleet-api.prd.na.vn.cloud.tesla.com, fleet-api.prd.eu.vn.cloud.tesla.com, fleet-api.prd.cn.vn.cloud.tesla.com (selected with --region)bin/tesla-fleet-api.py auth (must return "ok": true)door_lock / door_unlock change physical access to the car; remote_start_drive opens a 2-minute window where the car can be driven without a key. The CLI refuses to run without the exact --confirm text. No standing permission, no exceptions.--confirm naming the exact effect on first use per vehicle; reads and LOW actuations (honk, flash, trunk, preconditioning, navigation destination, software-update scheduling) need no confirmation.wake and frequent vehicle-data polling spend real money: poll sparingly and say the cost when the user asks for repeated checks.vehicle-data without --endpoints pulls the full state; pass --endpoints (semicolon-separated, e.g. charge_state;climate_state) to keep responses small and cheaper.signed_command envelope in the CLI follows the official Fleet API reference and is untested in this build. If the car rejects a command with a pairing error, the fix is virtual-key pairing; retrying will not help.🧪 Draft: written from Tesla's public Fleet API docs; not yet live-tested end-to-end.
Honesty flags: the vehicles, vehicle_data, wake_up, and signed_command paths are pinned in the official docs. The signed-command request envelope (routineName plus action parameters) follows the official reference but has not been exercised here, and signed commands cannot work at all until the app's public key is hosted and the vehicle pairs the virtual key. remote_start_drive and lock/unlock are HIGH and confirmation-gated in the CLI on every call.
© Anil-matcha, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in connectors/tesla-fleet-api of Anil-matcha/awesome-muse-connectors.
Open the folder on GitHubat commit d6dc5d8
Tesla Fleet API next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Tesla Fleet API this skillAnil-matcha/awesome-muse-connectors | 1.3k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Iot Fleetruvnet/ruflo | 74k | — | ~210 | Automated safety check: Pass | MIT | |
| My Teslasundial-org/awesome-openclaw-skills | 663 | — | ~2.3k | Automated safety check: Pass | None | |
| TeslaCraftOS-dev/CraftBot | 392 | 1 repos | ~917 | Automated safety check: Pass | MIT | |
| Codewhale Fleet Managercodewhale-hq/Codewhale | 41k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Fleetasheshgoplani/agent-deck | 1k | — | ~4.2k | Automated safety check: Pass | MIT |
ruvnet/ruflo
Create and manage Cognitum Seed device fleets with firmware policies
sundial-org/awesome-openclaw-skills
Control Tesla vehicles from macOS via the Tesla Owner API using teslapy (auth, list cars, status, lock/unlock, climate, charging, location, and extras).
CraftOS-dev/CraftBot
Control your Tesla vehicles - lock/unlock, climate, location, charge status, and more.
codewhale-hq/Codewhale
Triages and manages Codewhale fleet runs and workers with typed commands, classifying failures and choosing a safe restart, resume or escalation.
asheshgoplani/agent-deck
Fan out a fleet of independent agent-deck child sessions from inside a session and check their progress non-blockingly.
asgeirtj/system_prompts_leaks
Shows one digest of coding-agent sessions across your connected machines and lets you open, read, steer, approve, stop and close them, over Herdr, tmux or MSP.
Anil-matcha/awesome-muse-connectors
List Airtable bases, read table records, and add records. An agent skill from Anil-matcha/awesome-muse-connectors.
Anil-matcha/awesome-muse-connectors
Stock quotes and daily price history. An agent skill from Anil-matcha/awesome-muse-connectors.
Anil-matcha/awesome-muse-connectors
Search travel with Amadeus: flight offers and prices, airport autocomplete, hotel offers, cheapest dates.
Anil-matcha/awesome-muse-connectors
Search B2B contacts with Apollo.io: find people by title and company, enrich contacts and companies.
Anil-matcha/awesome-muse-connectors
Read and control Aqara smart home devices: plugs, switches, lights, AC, locks, curtains, scenes.
Anil-matcha/awesome-muse-connectors
Read and manage Asana tasks: my tasks, task details, create tasks.
Control Tesla vehicles through the official Tesla Fleet API: lock/unlock, remote start, charge control, preconditioning, honk, trunk. Tesla Fleet API is an agent skill from Anil-matcha/awesome-muse-connectors. Control Tesla vehicles through the official Tesla Fleet API: lock/unlock, remote start, charge control, preconditioning, honk, trunk.
Tesla Fleet API fits situations like: phrases: tesla car; precondition tesla.
Run `npx skills add Anil-matcha/awesome-muse-connectors --skill tesla-fleet-api -a claude-code`. Or copy the skill folder (connectors/tesla-fleet-api in Anil-matcha/awesome-muse-connectors) into .claude/skills/tesla-fleet-api in your project. Claude Code loads it when a task matches its description.
Run `npx skills add Anil-matcha/awesome-muse-connectors --skill tesla-fleet-api -a codex`. Or copy the skill folder (connectors/tesla-fleet-api in Anil-matcha/awesome-muse-connectors) into .agents/skills/tesla-fleet-api in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Anil-matcha/awesome-muse-connectors --skill tesla-fleet-api -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tesla-fleet-api, .gemini/skills/tesla-fleet-api, .github/skills/tesla-fleet-api and .opencode/skills/tesla-fleet-api in your project.
Going by SKILL.md and its folder, Tesla Fleet API needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Tesla Fleet API is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Tesla Fleet API: Iot Fleet (ruvnet/ruflo, 74k stars), My Tesla (sundial-org/awesome-openclaw-skills, 663 stars), Tesla (CraftOS-dev/CraftBot, 392 stars) and Codewhale Fleet Manager (codewhale-hq/Codewhale, 41k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Anil-matcha (a GitHub user) maintains it in Anil-matcha/awesome-muse-connectors, which has 1,346 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.
Source: Anil-matcha/awesome-muse-connectors on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.