show

show is a skill for Claude Code, Codex from ohm41321/luciazero. It costs 47 tokens per session (692 once invoked), scanned A, original, MIT.

A way to present code structure, changes, and verification results in a small, evidence-based view. It can use prose, file maps, diffs, call trees, or Mermaid diagrams, a text format for diagrams.

In plain words
What is it for?
Use it to explain code relationships, ownership, runtime flows, structural changes, or completed work. It can also organize build commands, test results, and remaining gaps.
Why use it?
It helps developers quickly understand what connects to what, what changed, and what proves the work is correct. It keeps confirmed facts separate from guesses and unknowns.

Skill for Claude CodeCodex

Part of the luciazero plugin — 12 skills, 1 agent, 7 hooks 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/ohm41321/luciazero/show
Any agent
npx skills add ohm41321/luciazero --skill show
Clone the repo
git clone --depth 1 https://github.com/ohm41321/luciazero

Made for: Claude Code, Codex.

Or install luciazero, the plugin that ships this one along with the rest of its 12 skills, 1 agent, 7 hooks.

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 show

README.md
[![agentmods](https://agentmods.dev/badge/skills/ohm41321/luciazero/show.svg)](https://agentmods.dev/skills/ohm41321/luciazero/show)
Your own site
<a href="https://agentmods.dev/skills/ohm41321/luciazero/show"><img src="https://agentmods.dev/badge/skills/ohm41321/luciazero/show.svg" alt="Measured on agentmods" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 692 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 $0.00047 $0.00692
Opus 5 $0.00023 $0.00346
Sonnet 5 $0.00009 $0.00138
Haiku 4.5 $0.00005 $0.00069

Measured 5d ago against content hash e6fc17e3eea6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

show 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 5d 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/show/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Show

Answer at a glance: What connects to what? What changed? What proves it? Build an evidence view, not a decorative diagram; source, diff, and command output remain ground truth.

1. Set the focus

Use the request and repository context. Do not ask for details that can be discovered from the repository. State the smallest boundary that answers the question. Gather only:

  • definitions, callers, consumers, configuration, and ownership;
  • current diff or before/after revisions;
  • verify command, exit code, decisive output, and coverage gaps.

Never expose private chain-of-thought. Show observable evidence and conclusions.

2. Normalize the evidence

Keep five kinds: Entities, Relations, Changes, Proof, and Gaps. Label inference as ? inferred; never draw a guessed edge as fact.

3. Choose the smallest useful view

Prefer the first form that carries the relationship clearly:

  • decision → compact pseudocode;
  • runtime calls → call tree;
  • ownership → shallow tree;
  • 3+ interacting parts → Mermaid flow/sequence;
  • structural change → before/after diff;
  • completion → requirement-to-proof map;
  • clear prose → one sentence or short list.

Use one primary view. Add another only for a different question. Reserve focused HTML for dense UI or interactive state. Keep HTML temporary unless the user asks to keep it, and open it only with permission.

4. Render with a stable grammar

A --> B             calls or moves data
A --owns--> B       named relation
+ / - / ~ item      added / removed / changed
[+] proven
[x] disproven
[?] unknown
[path/file:line]    source

Keep labels concrete and short. Omit unrelated detail. For Mermaid, keep node IDs simple, quote punctuation-heavy labels, and put noisy source pointers outside.

5. Attach evidence

Every important node or edge must be traceable to source, a diff/revision, or an exact command, exit code, and shortest decisive output. A green-looking view is not verification. If no command ran, write not run. If proof misses a claim, mark that claim [?] and name the gap; keep failed proof visible.

Read the full file on GitHub · 83 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 5d ago First seen · 83 lines · 47 tokens per session scan A e6fc17e3eea6

Subscribe to this mod's changes

show is a skill published in the GitHub repository ohm41321/luciazero (5 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 692 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.

Related

Other skills, from other repositories

neo4j-snowflake-graph-analytics-skill

Run Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN, Node2Vec, FastRP, GraphSAGE) directly inside Snowflake without moving data. Use when running graph algorithms against Snowflake tables via the Neo4j Snowflake Native App ("GDS Snowflake", "graph algorithms in Snowflake", "Neo4j Graph…

neo4j-contrib/neo4j-skills · 222 tokens

neo4j-agent-memory-skill

Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model…

neo4j-contrib/neo4j-skills · 223 tokens

neo4j-cypher-skill

Generates, optimizes, and validates Cypher 25 queries for Neo4j 2025.x and 2026.x. Use when writing new Cypher queries, optimizing slow queries, graph pattern matching, vector or fulltext search, subqueries, or batch writes. Covers MATCH, MERGE, CREATE, WITH, RETURN, CALL, UNWIND, FOREACH, LOAD CSV, SEARCH…

neo4j-contrib/neo4j-skills · 137 tokens

neo4j-document-import-skill

Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or…

neo4j-contrib/neo4j-skills · 187 tokens

neo4j-import-skill

Import structured data into Neo4j — LOAD CSV, CALL IN TRANSACTIONS, neo4j-admin database import full (offline bulk), apoc.load.csv/json, apoc.periodic.iterate, driver batch writes. Covers method selection, header file format, type coercion, null handling, ON ERROR modes, CONCURRENT TRANSACTIONS, pre-import constraint…

neo4j-contrib/neo4j-skills · 177 tokens

neo4j-vector-index-skill

Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity…

neo4j-contrib/neo4j-skills · 210 tokens