ladder-of-abstraction

ladder-of-abstraction is a command for Claude Code from aaronbassett/agent-foundry. It costs 34 tokens per session (758 once invoked), scanned A, original, MIT.

A decision-making command that restates a question at progressively broader and narrower levels. It uses two separate software agents: one moves toward higher-level goals, while the other moves toward concrete details.

In plain words
What is it for?
Use it to find the right level at which to make a decision and clarify whether you are solving the right problem.
Why use it?
It helps when the original question is poorly framed or when the available choices feel forced.

Command for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions subagents.

Part of the decision-making plugin — 1 skill, 7 commands, 2 agents shipped together

Good fit Use it to find the right level at which to make a decision and clarify whether you are solving the right problem.

Compare 6 commands from other repositories ↓
Install with agentmods
npx agentmods add commands/aaronbassett/agent-foundry/ladder-of-abstraction
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/aaronbassett/agent-foundry

Made for: Claude Code.

Or install decision-making, the plugin that ships this one along with the rest of its 1 skill, 7 commands, 2 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ladder-of-abstraction

README.md
[![agentmods](https://agentmods.dev/badge/commands/aaronbassett/agent-foundry/ladder-of-abstraction/github.svg)](https://agentmods.dev/commands/aaronbassett/agent-foundry/ladder-of-abstraction)
Your own site
<a href="https://agentmods.dev/commands/aaronbassett/agent-foundry/ladder-of-abstraction"><img src="https://agentmods.dev/badge/commands/aaronbassett/agent-foundry/ladder-of-abstraction/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ladder-of-abstraction

Your own site · 80×15
<a href="https://agentmods.dev/commands/aaronbassett/agent-foundry/ladder-of-abstraction"><img src="https://agentmods.dev/badge/commands/aaronbassett/agent-foundry/ladder-of-abstraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 758 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.00758
Opus 5 $0.00017 $0.00379
Sonnet 5 $0.00007 $0.00152
Haiku 4.5 $0.00003 $0.00076

Measured 12d ago against content hash 5fccfc64941b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

ladder-of-abstraction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/decision-making/commands/ladder-of-abstraction.md · 82 lines

How it starts

The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/decision-making:ladder-of-abstraction

When to use

Use this command when the question feels off, the options feel forced, or the decision keeps getting stuck. Reach for it when you suspect the problem is the framing, not the answer. The valuable output is often "the real decision is at a different altitude than where it was posed."

Cost tier

Low. Two parallel general-purpose subagents, one round. See references/cost-tiers.md.

Input

The question or decision that feels wrong + relevant context.

Workflow

  1. Spawn the Climbergeneral-purpose subagent with this prompt template (verbatim):

    Your job: reframe the question below at progressively higher levels
    of abstraction. Walk up a ladder.
    
    The question: [QUESTION]
    
    Context: [CONTEXT]
    
    For each rung on the way up, ask:
    - What are we actually trying to achieve with this?
    - What problem does that solve?
    - Is that the right problem to be solving?
    
    Return 3-4 rungs, each rephrasing the question at a higher level.
    Mark the highest rung where the question still feels actionable
    (above that, it becomes too vague to decide on).
    
    Do not answer the question. Only reframe it.
    
  2. Spawn the Descender in parallelgeneral-purpose subagent with this prompt template (verbatim):

    Your job: reframe the question below at progressively more concrete
    levels. Walk down a ladder.
    
    The question: [QUESTION]
    
    Context: [CONTEXT]
    
    For each rung on the way down, ask:
    - What would doing this look like on day one?
    - What is the first file you would touch?
    - What specific thing breaks or works?
    
    Return 3-4 rungs, each rephrasing the question at a more concrete
    level. Mark the lowest rung where the question is still
    generalizable (below that, it becomes a one-off detail).
    
    Do not answer the question. Only reframe it.
    
  3. Compare the two ladders — main thread compares the climb and descent. The real decision often lives at a different altitude than where it was posed:

    • Higher altitude — "you're not choosing between two databases, you're choosing whether to own infrastructure at all."
    • Lower altitude — "you're not choosing an architecture, you're choosing how to handle this one gnarly edge case."
    • Same altitude — both ladders confirm the question is well-posed at its current altitude.

Read the full file on GitHub · 82 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 82 lines · 34 tokens per session scan A 5fccfc64941b

Subscribe to this mod's changes

ladder-of-abstraction is a command published in the GitHub repository aaronbassett/agent-foundry (4 stars, last pushed 27d ago), licensed MIT. It adds 34 tokens to every session and 758 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.