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Device Data

Community
win4r
device_data

Read cached contacts and calendar events from Muse storage. Delete Muse's local copy of either source without modifying paired devices.

Overview

Publisherwin4r
RepositoryMuseAI-Skills
Skill namedevice_data
Stars
382
Forks
116
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by win4r on GitHub. Read the source before you install it.

Installation

Install the Device Data AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/win4r/MuseAI-Skills/tree/main/opt/hatch/skills/device-data
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/win4r/MuseAI-Skills.git /tmp/MuseAI-Skills
mkdir -p .claude/skills
cp -r /tmp/MuseAI-Skills/opt/hatch/skills/device-data .claude/skills/device_data
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Device Data in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Device Data on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Device Data is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Device Data

Purpose

Read cached contacts and calendar events directly from Muse storage, without contacting the device. Use it when a live read is not possible: the device is offline, or it does not offer contacts.search/calendar.search. Delete all locally stored contacts or calendar data when the user explicitly requests it. The tool does not contact or mutate a device.

Commands

bash
device-data --help
device-data contacts status
device-data contacts --delete-all
device-data contacts search --match-mode ranked \
                            [--query <text> [--phone-label <text>] \
                             [--locale <bcp-47>]] \
                            [--device <node_id>] [--limit <n>] [--offset <n>]
device-data calendar search [--query <text>] [--device <node_id>] \
                            [--start-date <YYYY-MM-DD>] \
                            [--end-date <YYYY-MM-DD>]
device-data calendar --delete-all

Both searches default to every eligible paired device. Use --device to scope to one and omit --query to list.

Contact search combines literal display-name, phone, and email substring matches with token-level exact, locale-aware personal-name nickname, and phonetic name matching. With no locale or an English locale, ranked search applies OACR-style fuzzy matching only after ordinary name matching returns nothing for that source device. It compares cleaned query tokens of at least four characters against the cleaned full name and canonical indexed name tokens, including simplified name variants, allows one edit, and returns at most three fuzzy candidates per source device. Explicit non-English locales retain the existing bounded Unicode full-name edit-distance fallback. Ranked name evidence feeds the downstream call-authorization policy. Fuzzy, partial, and phonetic evidence must pass bounded token revalidation before a complete singleton autodials; literal matches never autodial. Nickname singletons rely on the explicit locale-selected nickname map. Weak, ambiguous, incomplete, or unanchored matches still require confirmation.

Ranked results also include selection_evidence. Its spoken_name_collision_pairs identifies differently spelled plausible matches whose full names have strong phonetic coverage in both directions; a nonempty list sets requires_spoken_name_clarification, because spoken input has not selected between them. Collision entries carry a comma-separated spoken_spelling suitable for distinct TTS pronunciation in either a call or message clarification. Each contact includes phone_selection, which groups safely equivalent formatting variants into one destination and reports distinct_usable_phone_count and requires_phone_clarification. When --phone-label is present, requested_label_match_count reports how many distinct destinations carry that label or a recognized equivalent such as cell and mobile. Each option includes a TTS-safe spoken_suffix for the rare case where labels do not distinguish the saved lines.

Compound given names match as a unit: under the English default, Mary Ann can find a contact named Mary. Pass --locale to select the French, Italian, or Spanish nickname map when the user's locale is known; for example, Mamen finds María del Carmen with --locale es, and Coco finds Jean Claude with --locale fr. Language subtags ar, ja, zh, and ko disable nickname matching; every other locale without a dedicated map uses the English fallback. If the locale is unavailable, omit the flag to use that English nickname, fuzzy, and spoken-emoji default. Queries containing Chinese, Japanese, or Korean characters retain the bounded whole-name edit-distance fallback without requiring locale plumbing. Explicit non-English locales do not inherit English spoken-emoji labels. Phone matching ignores formatting and country-code differences when the final seven digits agree, but only for a phone-shaped query; digits embedded in names or emails do not activate phone matching. Ranked name search is order-independent, treats apostrophes and hyphens symmetrically, tolerates repeated or unmatched query words, and reports coarse match evidence (class, matched/total tokens, and unmatched tokens). Results are a single ranked, paginated list; they do not assert that one contact has been selected. Pass an explicitly requested phone type through --phone-label rather than including it in the name query. Phone numbers are ordered by the requested label first, then mobile, home, work, other, and custom labels. Results report phone_label_matched; a label miss does not exclude the contact. Organization and job title are included when available. Ranked results can be paged by advancing offset by the returned count while has_more is true. search_complete is false when the scoped contact corpus, bounded preprocessing, or a ranked result bound overflows, independently from pagination; inspect corpus_overflow, preprocessing_overflow, and result_overflow for the cause. fuzzy_overflow separately reports that English fuzzy recovery hit its non-pageable, per-source confirmation cap; it does not make an otherwise exact result incomplete.

Operating Rules

  1. Read fresh this turn; never answer a contacts or calendar question from memory.
  2. Treat device data as private user data: show only the fields the request needs, and do not infer sensitive attributes from contacts, locations, device names, or communication metadata.
  3. Use inclusive YYYY-MM-DD bounds for calendar dates, with the same date for a single day; all-day result end_date values are also inclusive. Read event times from the tagged time object: timed keeps the existing localized start_at / end_at fields and adds their UTC forms plus user_timezone, the IANA display zone used to render those local values; all_day has date-only values that must not be timezone-shifted. Both carry start_weekday / end_weekday, already resolved against the value beside them; use those and never work a weekday out from the date yourself. Contact reads likewise preserve synced_at and add semantic contact_synced_at or contact_sync_completed_at UTC/user-local forms.
  4. Treat an empty calendar result as authoritative only when cache.coverage is complete. For contacts, search_complete covers the bounded corpus and ranked result sets, not source freshness; consult contacts status when freshness matters and report partial or truncated results.
  5. If no record exists, say so. A device calendar is only that phone's view; check other connected calendars when relevant.
  6. When the user asks to delete contacts or calendar events without naming the target, do not infer the target. Confirm that the user means Muse's locally stored copy before deleting anything.
  7. After the user explicitly requests deletion of Muse's locally stored copy, run the command that matches the source: device-data contacts --delete-all or device-data calendar --delete-all. Do not enumerate record IDs.
  8. Both --delete-all commands delete the selected source from Muse storage across all devices. They delete cached records, source-ledger payloads, upload staging, and sync state. They preserve device pairing and every other data source. A later device sync can store new records for the deleted source.

Frequently asked questions

What does the Device Data AI skill do?

Read cached contacts and calendar events from Muse storage. Delete Muse's local copy of either source without modifying paired devices.

Why use Device Data on TypingMind?

Because you install it once and use it with any model. Device Data is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Device Data in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/win4r/MuseAI-Skills/tree/main/opt/hatch/skills/device-data. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Device Data?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Device Data?

As many as you like. As long as a model supports skills, you can use Device Data with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Device Data AI skill free?

It is published on GitHub by win4r. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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