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.
npx skills add ralfyishere/rules-with-receipts --skill deep-decompositiongit clone --depth 1 https://github.com/ralfyishere/rules-with-receiptsWrote 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/skills/ralfyishere/rules-with-receipts/deep-decomposition)<a href="https://agentmods.dev/skills/ralfyishere/rules-with-receipts/deep-decomposition"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/deep-decomposition/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/skills/ralfyishere/rules-with-receipts/deep-decomposition"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/deep-decomposition.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 | $0.00120 | $0.01500 |
| Opus 5 | $0.00060 | $0.00750 |
| Sonnet 5 | $0.00024 | $0.00300 |
| Haiku 4.5 | $0.00012 | $0.00150 |
Grade A, and why
Deep Decomposition 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 8d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Decomposition
Purpose
Turn one intractable task into a sequence of tractable ones. A task is decomposed well when every unit is small enough that you could start it right now and know when it's done — and the units are ordered so that the riskiest unknowns get resolved earliest, not discovered last.
When to use this skill
- A
plan-gateDepth-3 task: you can't hold the whole solution in one plan. - The task spans multiple domains (e.g., schema + API + UI; or research + analysis + writeup).
- You catch yourself writing vague plan steps ("handle the edge cases", "do the integration").
- Research or strategy questions that are really 4–6 questions wearing a trenchcoat.
When NOT to use this skill
- The task fits in a Depth-1 or Depth-2 plan. Decomposing it is procrastination with extra steps.
- The user asked for a quick draft or first pass — decomposition depth should follow
effort-calibration, not habit.
Operating procedure
Step 1 — State the end goal in one sentence. If you can't, go back to intent-clarity.
Step 2 — Inventory knowns and unknowns.
- Knowns: what you already have (files, facts, requirements, prior art).
- Unknowns: what you'd have to discover. Mark each unknown cheap (one command/read away) or expensive (needs experimentation or user input).
- Map the domain's dimensionality before building for its first cell. If the task
targets a space (markets, entities, file types, locales, plans), enumerate its axes
from the source of truth — an API, a listing, a schema — not from assumption; it is
usually one cheap query. Then build the general case (dynamic discovery, config-driven
values) rather than hardcoding the first slice. Tell: a hardcoded enumeration
(
ITEMS = [4 things], a fixed column list) where the real set is queryable and growing. Real case (2026-07-11): a dashboard was built for one entity × one dimension and re-scoped three times by the user; the full entity × dimension × type matrix was enumerable from the platform's API in two calls.
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.
- 8d ago First seen · 94 lines · 0 tokens per session scan A 642ea555f08c
Deep Decomposition is a skill published in the GitHub repository ralfyishere/rules-with-receipts (2 stars, last pushed 1mo ago), licensed MIT. It adds 120 tokens to every session and 1,500 once invoked, about $0.0006 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.
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