Prism Scan logo

Prism Scan

Community
Cranot
prism-scan

Structural analysis through dynamically generated cognitive lenses. Generates the optimal analytical lens for the specific code/artifact, then executes it. Finds conservation laws, structural invariants, and concrete bugs that vanilla analysis misses. Use on any code file, system design, or text artifact.

Overview

PublisherCranot
Repositorysuper-hermes
Skill nameprism-scan
Stars
504
Forks
31
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Cranot on GitHub. Read the source before you install it.

Installation

Install the Prism Scan AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Cranot/super-hermes/tree/master/skills/prism-scan
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Cranot/super-hermes.git /tmp/super-hermes
mkdir -p .claude/skills
cp -r /tmp/super-hermes/skills/prism-scan .claude/skills/prism-scan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prism Scan in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prism Scan on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prism Scan is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Prism Scan — Structural Analysis via Dynamic Cognitive Lenses

When to Use

Use on a single code file, system design, spec, or any text artifact when the user wants depth rather than a checklist — "what's structurally wrong with this", "what trade-off can't I escape here", "review this properly". Also the right entry point when the user names a focus ("focusing on security", "with emphasis on performance"). For maximum depth on an important artifact use /prism-full; to map which angles are worth taking first use /prism-discover.

You perform TWO steps. Both are mandatory. Do not skip either.

STEP 0: Check for prior constraint knowledge (growth)

Look for a .prism-history.md file in the current project directory. This file may or may not exist — if it does not exist, that is normal (it means this is the first analysis on this project, just proceed to Step 1).

If the file DOES exist, read it. It contains constraint reports from previous /prism-reflect analyses — what past analyses maximized, what they sacrificed, and what gaps remain. Use this history to inform your lens generation: if past analyses consistently sacrificed temporal analysis, weight your new lens toward temporal concerns. The agent grows by not repeating the same blind spots.

STEP 1: Cook the lens

You are a lens generator. Read the artifact the user provided. Based on what you see — and any constraint history from Step 0 — generate ONE optimal analytical lens that will force the deepest possible structural analysis of THIS specific artifact.

Study these scored examples of excellent lenses:

SCORED 9.5/10: "Identify every explicit choice this artifact makes. For each, name the alternative it invisibly rejects. Design a new artifact by someone who internalized this one's patterns but faced a different problem. Trace which transferred patterns create silent problems. Name the pedagogy law."

SCORED 9/10: "Extract every empirical claim this artifact embeds. For each, assume it is false. Trace the corruption. Build three alternatives, each inverting one claim. Predict which false claim causes the slowest, most invisible failure."

SCORED 9/10: "Run this artifact forward through 3 maintenance cycles. In each cycle: name what breaks, what calcifies into permanent behavior, and what knowledge is lost. After all cycles, derive the conservation law: what trade-off persists no matter how the code evolves?"

Your lens must:

  • Be specific to what you observe in this artifact (not generic)
  • If the user specified a FOCUS (e.g., "focusing on security" or "with emphasis on performance"), tailor the lens to that direction while still forcing structural depth
  • Force construction (build something, then diagnose what the construction reveals)
  • End with concrete outputs (bugs, laws, predictions — not just observations)
  • Be 75-200 words (minimum 75 — below this, models enumerate instead of analyze)

Output your lens under the heading "## Generated Lens" — show it so the user can see what was cooked.

STEP 2: Execute the lens

Now execute your generated lens against the artifact. Follow every instruction in the lens. Output the complete analysis. Do not summarize, do not ask permission, do not skip steps.

End with a concrete findings table: location, what breaks, severity, and whether the finding is fixable or structural (a property of the problem space that persists across all implementations).

After the findings table, append a brief constraint footer:

---
CONSTRAINT NOTE: This analysis maximized [what your lens focused on].
It did not examine: [1-2 specific alternative angles].
For deeper analysis: /prism-full | For meta-analysis: /prism-reflect

Reliability Note

For guaranteed single-shot execution (no agentic loops), use --tools "" flag when running via Claude CLI. This ensures the model executes the full analysis in one response rather than splitting into multiple turns.

Proven Prisms (reference material)

STEP 1 always cooks a fresh lens — that is the point of this skill, and these files do not change it. They ship alongside the skill as pre-validated alternatives for the two cases where a fixed lens is wanted: the user asks for a specific prism by name, or asks for a scored/reproducible lens instead of a generated one. Load one on demand with skill_view("prism-scan", "<path>").

PathFindsScore
references/error_resilience.mdCorruption cascades — silent exits, deferred failures, state corruption10.0/10
references/l12.mdConservation laws + meta-laws + concrete bugs9.8/10
references/optimize.mdCritical-path tracing; safe fixes (less work) vs unsafe (skipped work)9.5/10
references/identity.mdWhat the artifact claims to be vs what it is9.5/10
references/deep_scan.mdInformation destruction, laundering, silent transformation9.0/10
references/claim.mdAssumption inversion — what if the embedded claims are false9.0/10
references/simulation.mdTemporal prediction — what breaks, calcifies, and is lost over time9.0/10

Each file is a complete standalone analytical program and also works as a system prompt outside Hermes.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prism Scan AI skill do?

Structural analysis through dynamically generated cognitive lenses. Generates the optimal analytical lens for the specific code/artifact, then executes it. Finds conservation laws, structural invariants, and concrete bugs that vanilla analysis misses. Use on any code file, system design, or text artifact.

Why use Prism Scan on TypingMind?

Because you install it once and use it with any model. Prism Scan is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prism Scan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Cranot/super-hermes/tree/master/skills/prism-scan. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Prism Scan?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prism Scan?

As many as you like. As long as a model supports skills, you can use Prism Scan with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prism Scan AI skill free?

Yes. It is published on GitHub by Cranot under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇