Agent skill

Agent Decision

by ntorga in ntorga/agent-starter-kit

Deterministic self-evaluation rubric for decision escalations — scored every run using the FRAME framework.

MITAuto-check passedEducation

Install Agent Decision

skills CLI
$ npx skills add ntorga/agent-starter-kit --skill agent-decision -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ntorga/agent-starter-kit agent-decision --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ntorga/agent-starter-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agent-decision .claude/skills/agent-decision && rm -rf skills-src

Use ~/.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/

Facts

Skill name
agent-decision
GitHub stars
146
Token cost
~1.7k tokens
SKILL.md length
896 words
Files
1
Skills in repo
21
Repo updated
First seen
Licence
MIT

At a glance

Deterministic self-evaluation rubric for decision escalations — scored every run using the FRAME framework.

  • Works in 7 steps: Classify the ambiguity. When you… → Apply the branch. → 1-3-1 analysis. When escalating, build… → …
  • Tasks that involve Quizzes and assessments
  • SKILL.md covers Purpose, Procedure, FRAME Rubric and Guardrails
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agent Decision is an agent skill from ntorga/agent-starter-kit. Deterministic self-evaluation rubric for decision escalations — scored every run using the FRAME framework.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Education, covering Quizzes and assessments. The repository describes itself as: The scaffold for your multi-model, personalized Natural Language AI Harness (NLAH) . The licence is MIT.

When your agent uses it

  • Tasks that involve Quizzes and assessments

Example prompts

  • “/agent-decision”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Classify the ambiguity. When you encounter missing or unclear information, determine which of these 5 types applies
  2. Apply the branch.
  3. 1-3-1 analysis. When escalating, build your escalation in this order
  4. Apply the output template. Format the escalation exactly as follows
  5. Score each criterion. After preparing the escalation, read the FRAME rubric below and assign a score of 0, 1, or 2 to each letter. Show…
  6. Apply the hard-fail rule. If any letter scores 0, do not deliver — go to step 7 immediately.
  7. Determine action by total score

What it can do on your machine

Read from SKILL.md and the folder at commit 851e942. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Decision loads about 1.7k tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 896 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from ntorga/agent-starter-kit at commit 851e942, republished under its MIT licence (© ntorga). 896 words, ~1,687 tokens.

Download SKILL.mdSave it as .claude/skills/agent-decision/SKILL.md (or your agent's skills folder).
name
agent-decision
description
Deterministic self-evaluation rubric for decision escalations — scored every run using the FRAME framework.
usedBy
all
version
0.2.2
lastUpdated
2026-09-12

Purpose

This skill defines the procedure agents follow when encountering ambiguity during execution. It enforces a 5-step decision pipeline — classify, define problem, generate options, pick one, format output — and a self-review rubric (FRAME) scored before any escalation is presented to the user. The rubric gates delivery by total score and enforces a hard-fail on any single zero.

Procedure

  1. Classify the ambiguity. When you encounter missing or unclear information, determine which of these 5 types applies:

    • Approach choice — multiple valid approaches exist and the user did not specify one, but all lead to the same outcome. Example: whether to use a helper function or inline the logic.
    • Minor ambiguity — the intent is clear but a detail is vague and a reasonable default exists. Example: "add logging" without specifying log level — default to info.
    • Ambiguous requirement — the user's intent could mean two or more meaningfully different things. Example: "make it faster" could mean optimize the algorithm, add caching, or reduce payload size.
    • Missing information — a required fact is absent and cannot be inferred. Example: "deploy to the server" with no server specified and no convention to fall back on.
    • Risk confirmation — the action is destructive, expensive, or irreversible and the user has not explicitly authorized it. Example: dropping a database table, force-pushing to main.
  2. Apply the branch.

    • Approach choice and minor ambiguity → proceed with a documented default. Record your choice in your handoff. Do not block.
    • Ambiguous requirement → escalate using the 1-3-1 method (step 3) and output template (step 4).
    • Missing information or risk confirmation → stop and ask directly. For non-interactive sessions, return a handoff explaining the gap.
    • The dividing line: if the ambiguity changes what you build, escalate. If it only changes how you build the same thing, proceed.
  3. 1-3-1 analysis. When escalating, build your escalation in this order:

    • 1 problem — Write one sentence stating the single problem.
    • 3 options — Generate three meaningfully different approaches. Each with a one-sentence trade-off.
    • 1 recommendation — Pick the strongest option. Write the reason.
  4. Apply the output template. Format the escalation exactly as follows:

    The [problem statement in one sentence].
    
    I'm [what I'm working on] and [where I am in the process].
    
    The choice is [decision in plain language]. No jargon.
    
    RECOMMENDATION: [A|B|C] because [reason].
    
    A) [option] — [one-sentence trade-off]
    B) [option] — [one-sentence trade-off]
    C) [option] — [one-sentence trade-off]
  5. Score each criterion. After preparing the escalation, read the FRAME rubric below and assign a score of 0, 1, or 2 to each letter. Show the scoring breakdown to yourself (internal reasoning, not to the user).

  6. Apply the hard-fail rule. If any letter scores 0, do not deliver — go to step 7 immediately.

  7. Determine action by total score:

    • 9 – 10 — DELIVER — Escalation meets all criteria. Deliver to user.
    • 7 – 8 — FIX the scored < 2 criteria. a. Identify which letters scored below 2. b. Fix those gaps automatically (do NOT consult the user). c. Re-score, then deliver if 9-10. d. If still below 9, retry once more. e. After 2 failed fix attempts, yield with the current state, rubric scores, and blocking letters.
    • 0 – 6 — RESTART — The escalation is fundamentally broken. Discard and rebuild with corrected understanding, or yield with an explanation of what went wrong.

FRAME Rubric

Show full SKILL.md (404 more words)Show less
F — FLAG THE BRANCH

Did I classify the ambiguity type and apply the correct branch (proceed vs escalate)?

  • 0 — No classification performed. Jumped straight to acting or asking without determining the ambiguity type.
  • 1 — Classified the type but picked the wrong branch: proceeded when should escalate (ambiguous requirement, missing info, risk), or escalated when should proceed (approach choice, minor ambiguity).
  • 2 — Classified correctly. Applied the right branch: proceed for approach choice and minor ambiguity; escalate for ambiguous requirement; stop-and-ask for missing info and risk confirmation.
R — ROOT THE PROBLEM

Did I state exactly one problem before generating solutions?

  • 0 — Skipped problem definition. Jumped from noticing ambiguity straight to options or open-ended questions.
  • 1 — Stated a problem but it is vague ("something is unclear") or bundles multiple problems into one statement.
  • 2 — One sentence. One problem. Specific enough that someone could evaluate whether a solution addresses it.
A — ARRAY THE OPTIONS

Did I generate 3 meaningfully different options?

  • 0 — Options are all variants of the same approach. Or presented fewer than 3. Or presented more than 5.
  • 1 — Three options exist but two are too similar to be meaningful alternatives. No trade-off stated per option.
  • 2 — Three options. Each takes a distinctly different approach. Each has a one-sentence trade-off.
M — MAKE THE PICK

Did I choose one option and state the reason?

  • 0 — Listed options without picking one. Or picked one but gave no reason. Or used "it depends" or hedging language.
  • 1 — Picked one but the reason is generic ("it's better") or does not connect to the root problem.
  • 2 — Exactly one recommendation stated as RECOMMENDATION: [option] because [reason]. The reason traces back to the root problem defined in R.
E — ENFORCE THE FORMAT

Did I follow the 5-part structured question format exactly?

  • 0 — Wrong format entirely. Asked an open-ended question. Missing RECOMMENDATION line.
  • 1 — Most sections present but one is wrong or out of order (e.g., context statement missing, or Options numbered instead of lettered A/B/C).
  • 2 — All sections in exact order: Problem → Context → Choice → RECOMMENDATION → A) B) C). Lettered options (not numbered). One sentence per option trade-off.

Guardrails

  • Never ask open-ended questions when concrete options exist
  • Never present options without a recommendation
  • Never treat every gap as a blocker (approach choice + minor ambiguity proceed inline)
  • Simple yes/no and single-fact clarifications are exempt from this format
  • The rubric is fixed — do not add or remove criteria
  • Never deliver with any letter scoring 0

© ntorga, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/agent-decision of ntorga/agent-starter-kit.

Open the folder on GitHubat commit 851e942

Compare with similar skills

Agent Decision 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.

Agent Decision compared with similar skills
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Agent Decision this skillntorga/agent-starter-kit146—~1.7kAutomated safety check: PassMIT
DeepTutor CLIHKUDS/DeepTutor41k—~2.3kAutomated safety check: PassApache-2.0
AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch65k—~2kAutomated safety check: PassMIT
Codebase to Coursezarazhangrui/codebase-to-course5.7k—~4.4kAutomated safety check: PassNone
AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch65k—~2.1kAutomated safety check: PassMIT
Scholar EvaluationK-Dense-AI/claude-scientific-writer2.4k2 repos~2.9kAutomated safety check: NotesMIT

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Categories

Questions about Agent Decision

What does Agent Decision do?

Deterministic self-evaluation rubric for decision escalations — scored every run using the FRAME framework. Agent Decision is an agent skill from ntorga/agent-starter-kit. Deterministic self-evaluation rubric for decision escalations — scored every run using the FRAME framework.

When should I use Agent Decision?

Agent Decision fits situations like: tasks that involve Quizzes and assessments.

How do I install Agent Decision in Claude Code?

Run `npx skills add ntorga/agent-starter-kit --skill agent-decision -a claude-code`. Or copy the skill folder (skills/agent-decision in ntorga/agent-starter-kit) into .claude/skills/agent-decision in your project. Claude Code loads it when a task matches its description.

How do I install Agent Decision in Codex?

Run `npx skills add ntorga/agent-starter-kit --skill agent-decision -a codex`. Or copy the skill folder (skills/agent-decision in ntorga/agent-starter-kit) into .agents/skills/agent-decision in your project. Codex loads it when a task matches its description.

Can I use Agent Decision in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ntorga/agent-starter-kit --skill agent-decision -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-decision, .gemini/skills/agent-decision, .github/skills/agent-decision and .opencode/skills/agent-decision in your project.

What does Agent Decision need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Decision is instructions for the agent only.

Does Agent Decision access the network?

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.

Is Agent Decision safe to install?

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.

What licence does Agent Decision use?

Agent Decision is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Decision use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Decision?

Skills that share tags, products or a category with Agent Decision: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 65k stars), Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars) and AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Decision?

ntorga (a GitHub user) maintains it in ntorga/agent-starter-kit, which has 146 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on September 12, 2026.

Source: ntorga/agent-starter-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.