agentleFS
GitHubKnown publishergithub/awesome-copilot/skills/on-device-delegate/SKILL.md

on-device-delegate

Skill41k starsChanged 21 days agoMITView on GitHub

Saves cloud-model context by briefing one bounded, read-heavy sub-task (more than about 25 KB of prose, or many files that no single command can answer) to the local-coding plugin''s on-device worker, which runs on the local MAI Code 1.1 Flash model. Use for log and test triage, summarizing many files, or extracting facts from large inputs. When the user names this skill, call the skill tool in the same response as your first other tool call, never as a separate step.

Trending

More talk lately.about the whole repository

Dec 2025: 10 mentionsJan 2026: 4 mentionsFeb 2026: 7 mentionsMar 2026: 12 mentionsApr 2026: 6 mentionsMay 2026: 2 mentionsJun 2026: 2 mentionsJul 2026: 2 mentionsAug 2026: 5 mentionsSep 2026: 9 mentionsOct 2026: 5 mentionsNov 2026: 1 mention
Dec 2025Jun 2026Nov 2026
Mentions a month of the github/awesome-copilot repository that our weekly crawl found. There is no adoption data for this file yet.

Who's talking about github/awesome-copilot

The biggest 5 of 90 mentions linking to the repository that our crawl found. Each line is their own headline.

See all 90

Talked about on Bluesky, GitHub, YouTube, Hacker News and the web (90 times).

On Bluesky · 41

36 more on Bluesky

On GitHub · 34

29 more on GitHub

On YouTube · 5

On the web · 5

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Gate on what you would read: If one command or a short script can compute the answer, do it yourself now: no plan, no notes about this …

    line 15
  2. Distinctive:

    Brief: Do not read the big input first.

    line 24
  3. Distinctive:

    Check: confirm the lines match the format and spot-check one count with a single search.

    line 65

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

On-Device Delegate

Send the bulky reading in a task to the on-device worker, then check its answer once. The main session keeps planning and answering. The worker reads the big inputs on the local model, so they never enter the main context.

Steps

  1. Gate on what you would read. If one command or a short script can compute the answer, do it yourself now: no plan, no notes about this skill. Delegate only when you would otherwise READ more than about 25 KB of text into your context (prose, many files). Never delegate security review or design decisions.
  2. Check the worker. The worker is the custom agent local-coding:on-device-worker (the plugin id). If the task tool lists it as plain on-device-worker instead (copied into an agents folder), use that. If neither is available, work normally.
  3. Brief. Do not read the big input first. Make ONE task call:
    • name: "on-device-<short-kebab>"
    • description: 3-5 words
    • agent_type: the worker id from step 2
    • mode: "sync"
    • model: the on-device model id named in the session's routing line, exactly (if there is none: "winml-azureml-mai-code-1.1-flash-on-device/MAI-Code-1.1-Flash-On-Device"). The worker does not pin a model, so without this it runs on your own model.
    • prompt, exactly:

      GOAL: <one sentence>
      INPUTS: <exact paths>
      RULES: Read only INPUTS. At most 8 tool calls. Edit nothing but OUTPUT. No questions.
      OUTPUT: <file path and exact format, or: reply only>
      ANSWER: Reply ONCE with only <exact format>, then stop.
      If you cannot, reply only: UNABLE: <reason>
  4. Check once. At most ONE cheap check (format, a count, or one spot check). Never recompute the answer or re-read the input. If the answer is wrong, UNABLE, or the task call fails: do it yourself. Then finish.

Example

User: "Use the on-device-delegate skill. From the logs under logs\, list every distinct error message with its count, most frequent first."

Task prompt:

GOAL: Count each distinct error message in the logs.
INPUTS: logs\
RULES: Read only INPUTS. At most 8 tool calls. Edit nothing but OUTPUT. No questions.
OUTPUT: reply only
ANSWER: Reply ONCE with only lines of the form "<count> <message>", most frequent first, then stop.
If you cannot, reply only: UNABLE: <reason>

Check: confirm the lines match the format and spot-check one count with a single search. Then answer the user.

Limits

  • The worker can return a confident but wrong answer. The single check catches format errors, not every wrong fact. Tell the user what you checked.
  • Expect minutes, not seconds, per delegated task, especially on the first call while the local model loads.

Setup scripts

scripts/ holds two setup scripts for the user, described in the local-coding plugin README. They are not part of a task: never run them while you work on the user's request.

  • scripts/Set-LocalOutputCap.ps1 caps the local model's output tokens in the user's providers.json.
  • scripts/Update-LocalCodingPlugin.ps1 installs or updates the plugin, then runs the cap script.

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.