AugmentClaude

Decision Expert

Classify content and score decisions with confidence levels and structured answers.

Installation

  1. Make sure Claude is on your device and in your terminal.

    Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

    Already have it? Skip ahead.

  2. Paste into Claude Code or into your terminal.

    This copies the whole skill folder into ~/.claude/skills/yao-decision-yaoapp/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

    Faster alternative (instruction-only skills)

    Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

    Quick install (SKILL.md only)
    Sign up to copy
  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. Just ask Claude.

    Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

Typed decision expert. ALWAYS invoke this skill when you need structured classification, scoring, or probability estimation — routing tickets, grading content, detecting intent, or any task requiring typed answers with calibrated confidence.

What this skill does

Decision Tools

Use these tools when you need structured, typed decisions rather than free-form LLM text. Decision models return machine-readable answers with probabilities and confidence scores.

decision_decide

Send a state (context) and one or more typed questions to a decision model.

Question Types

TypePurposeCriteriaAnswer Fields
choiceClassify into one of N options{option: description} mapchoice, probabilities, confidence
scoreRate on a scale[level_labels] arrayscore, legend, probabilities, confidence
noulEstimate a probability (0-1)(none)noul

Basic usage (choice + noul):

tai tool decision_decide --state "Customer says: my billing is wrong, I was charged twice" --questions '{"department":{"type":"choice","instructions":"Route to the right department","criteria":{"billing":"Payment issues","technical":"Product bugs","sales":"Purchase inquiries"}},"is_urgent":{"type":"noul","instructions":"Is this urgent?"}}'

With score question:

tai tool decision_decide --state "The agent resolved the issue in 2 minutes with a clear explanation" --questions '{"quality":{"type":"score","instructions":"Rate the support quality","criteria":["Poor","Below average","Average","Good","Excellent"]}}'

With explicit provider:

tai tool decision_decide --state "some context" --questions '{"q1":{"type":"noul","instructions":"Is this positive?"}}' --provider t862699292876.typesafe

With model override:

tai tool decision_decide --state "some context" --questions '{"q1":{"type":"noul","instructions":"Is this positive?"}}' --model jev-latest

Batch decisions (multiple states, same questions):

tai tool decision_decide --states '["Customer A says billing is wrong","Customer B loves the product","Customer C wants a refund"]' --questions '{"sentiment":{"type":"choice","instructions":"Classify sentiment","criteria":{"positive":"Happy","negative":"Unhappy","neutral":"Neither"}}}'

Reading from files:

tai tool decision_decide --state_file customer_message.txt --questions_file routing_questions.json
ParameterTypeRequiredDescription
statestring/object/arrayyes*The context for the decision (*mutually exclusive with states)
questionsJSON objectyesMap of question_id to question definition
modelstringnoModel name override (default: provider's configured model)
providerstringnoProvider connector ID (default: decision role or first available)
timeoutintegernoTimeout in seconds (default: 60)
statesarrayno*Array of state items for batch decisions (*mutually exclusive with state)
state_filestringnoRead state from file (tai CLI only)
questions_filestringnoRead questions from JSON file (tai CLI only)

Response Format

Single state:

{
  "answers": {
    "department": {
      "type": "choice",
      "choice": "billing",
      "probabilities": {"billing": 0.92, "technical": 0.05, "sales": 0.03},
      "confidence": 0.89
    },
    "is_urgent": {
      "type": "noul",
      "noul": 0.73
    }
  },
  "model": "jev-1.13.0",
  "usage": {"input_tokens": 245, "output_tokens": 38}
}

Note: model in the response is the resolved concrete model ID (e.g. jev-1.13.0), not the alias you requested (e.g. jev-latest).

Batch states:

{
  "results": [
    {"state_index": 0, "answers": {...}, "model": "jev-1.13.0", "usage": {...}},
    {"state_index": 1, "answers": {...}, "model": "jev-1.13.0", "usage": {...}},
    {"state_index": 2, "error": "timed out after 60s"}
  ]
}

decision_providers

List available decision providers and their models.

tai tool decision_providers
ParameterTypeRequiredDescription
capabilitystringnoFilter by capability (default: decision)

Returns a list of providers with available models, connector IDs, and availability status.

Constraints

  • state and states are mutually exclusive; provide one or the other.
  • Each question must have type (choice/score/noul) and instructions.
  • criteria is required for choice (map) and score (array); noul must not have criteria.
  • Batch states must not contain empty strings or null values.
  • timeout defaults to 60s; values ≤ 0 are treated as the default.
  • Batch mode runs up to 5 concurrent calls; timeout applies to the entire batch.
  • Response model is the resolved concrete model ID (e.g. jev-1.13.0), not the requested alias.

Related skills