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.
git clone --depth 1 https://github.com/aaronbassett/agent-foundryWrote 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.
[](https://agentmods.dev/commands/aaronbassett/agent-foundry/ladder-of-abstraction)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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
-
Spawn the Climber —
general-purposesubagent 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. -
Spawn the Descender in parallel —
general-purposesubagent 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. -
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.
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.
- 12d ago First seen · 82 lines · 34 tokens per session scan A 5fccfc64941b
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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.