prism

prism is a skill for Claude Code, Codex from LilMGenius/paperthin. It costs 98 tokens per session (653 once invoked), scanned A, original, MIT.

A review method that examines one claim, plan, or file through two to five independent viewpoints, each focused on a different type of failure. It compares their conclusions instead of averaging them.

In plain words
What is it for?
Reviewing a proposed change or other artefact, assigning distinct review lenses, recording a short verdict for each, and finding where the reviewers agree or disagree.
Why use it?
It helps expose disagreements between concerns such as correctness, security, readability, cost, or misuse, and identifies the one question that would resolve an unclear decision.

Skill for Claude CodeCodex

Part of the paperthin plugin — 28 skills shipped together

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.

agentmods
npx agentmods add skills/lilmgenius/paperthin/prism
Any agent
npx skills add LilMGenius/paperthin --skill prism
Clone the repo
git clone --depth 1 https://github.com/LilMGenius/paperthin

Made for: Claude Code, Codex.

Or install paperthin, the plugin that ships this one along with the rest of its 28 skills.

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 prism

README.md
[![agentmods](https://agentmods.dev/badge/skills/lilmgenius/paperthin/prism.svg)](https://agentmods.dev/skills/lilmgenius/paperthin/prism)
Your own site
<a href="https://agentmods.dev/skills/lilmgenius/paperthin/prism"><img src="https://agentmods.dev/badge/skills/lilmgenius/paperthin/prism.svg" alt="Measured on agentmods" height="20"></a>
Per session 98 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 653 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00098 $0.00653
Opus 5 $0.00049 $0.00327
Sonnet 5 $0.00020 $0.00131
Haiku 4.5 $0.00010 $0.00065

Measured 4d ago against content hash c7ed6a24e508, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

prism 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 4d 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.

skills/mesh/prism/SKILL.md · 35 lines

How it starts

The opening of the file, as written. The whole thing — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Split one artifact across independent lenses and return where they converge and where they don't.

Goal

The result is a picture of where the lenses agree, where they disagree, and the smallest question that would resolve the disagreement. Not a checklist, and never an average — averaging the lenses is the failure this skill exists to prevent.

Workflow

  1. Read the artifact end to end before choosing the lenses.
  2. Choose one lens per genuinely distinct failure mode — correctness, security, readability, cost, adversarial-user, and the like. Two candidate lenses that share a failure mode are one lens, not two. In practice this lands between 2 and 5: below 2 there is nothing to converge, and past 5 you are almost always padding with lenses that overlap. There is no default count — the artifact's distinct failure modes set it.
  3. Through each lens, produce a one-line verdict — pass, fail, or unclear — with its single most load-bearing reason. No hedging list; if a reason isn't load-bearing, drop it.
  4. Compare the verdicts and group them: full agreement, agreement for different reasons, disagreement.
  5. Where there is disagreement, name the single next question whose answer would move it into agreement or a clean split with owners. That question is the output, not the individual verdicts.
  6. Where every lens agrees, return the shared verdict with the reasons that were load-bearing for at least two lenses.

Rules

  • Independent lenses, not roles. A "security reviewer" and a "senior security reviewer" are one lens, not two.
  • One reason per lens — the load-bearing one. A lens that returns a list is hedging.
  • Disagreement is the product. A prism that agrees on everything did not need to be run.
  • Never average. If two lenses split, the answer is the question that resolves the split, not the midpoint of their verdicts.
  • Draw the lenses fresh for the artifact in hand. A lens useful for a spec may be useless for a migration; don't carry lenses across artifacts.
  • Don't manufacture disagreement. A run that finds none returns the shared verdict and stops.
  • User-invoked on purpose: plural lenses are an opt-in spend, and their convergence must never masquerade as automatic proof, so this fires only when deliberately reached for.

Read the full file on GitHub · 35 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. 4d ago First seen · 35 lines · 98 tokens per session scan A c7ed6a24e508

Subscribe to this mod's changes

prism is a skill published in the GitHub repository LilMGenius/paperthin (1,054 stars, last pushed 16d ago), licensed MIT. It adds 98 tokens to every session and 653 once invoked, about $0.0005 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 2026-08-30.

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