agentleFS
Sign inStart free
DAAF-Contribution-CommunityDAAF-Contribution-Community/daaf/.claude/skills/typesafe-ai/SKILL.md

typesafe-ai

Skill239 starsChanged 10 days agoLGPL-3.0View on GitHub

Typesafe AI's Jev / System One typed-judgment API, called over raw HTTP from DAAF scripts. Jev generates no text: it takes a `state` plus a map of typed `questions` and returns vendor-calibrated probabilities — choice (pick one option, with a distribution and confidence), score (position on 2-10 ordered levels, likewise), and noul (probability a yes/no statement is true); an experimental box type locates something in an image. Load when a task mentions Typesafe, TypeSafe AI, Jev, System One, `api.typesafe.ai`, or `TYPESAFE_API_KEY`, or wants probability-typed judgments over text or images — classify, rank, verify, or locate, into values code can threshold on. This is an external third-party inference API reached with `requests`, not a DAAF data source or dataset; for research data acquisition use the education/election data-source skills. Not for generating prose, code, or explanations. Not for OCR, transcription, or local image processing (scanned PDFs: pdf-reading).

  • Reads credentials

35 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use computer-use, diagnosing-bugs and 8 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.
  • HK-hubSkill

    computer-use

    Use Orca's computer-use CLI to inspect and operate local desktop app windows through accessibility trees, screenshots, and safe UI actions. Use for desktop app interaction: list apps/windows, get app state, read visible UI, click controls, type, press keys, scroll, drag, set values, or perform accessibility actions. Also use for browser windows, webviews, Orca app UI, or other desktop UI. Triggers include "computer use", "orca computer", "read Spotify", "read Slack", "control/click/read in a desktop app", and "get app state".

    Also in 3 projects that use this

  • GoogleSkill

    diagnosing-bugs

    Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

    Also in 3 projects that use this

  • Hugging FaceSkill

    implement

    Make tests pass. Invoke after /write-tests produces failing tests.

    Also in 4 projects that use this

  • GoogleSkill

    codebase-design

    Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

    Also in 3 projects that use this

  • MicrosoftSkill

    grill-with-docs

    Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.

    Also in 3 projects that use this

  • VercelSkill

    find-skills

    Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

    Also in 3 projects that use this

  • +3 more from google

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 35 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

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

Licensed with conditions — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 35 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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.

Check your setup, free