table

table is a skill for Claude Code, Codex from adilkalam/orca. It costs 13 tokens per session (1,444 once invoked), scanned A, original, MIT.

A formatter for making Markdown tables line up correctly, including tables containing characters with different display widths.

In plain words
What is it for?
Use it after creating a Markdown table to format its columns consistently.
Why use it?
It removes alignment problems caused by manually spacing table columns or by characters such as emoji and Asian-language text.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: reads .claude/ paths; positional $N argument.

Good fit Use it after creating a Markdown table to format its columns consistently.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/adilkalam/orca/ascii-tables
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.

Any agent
npx skills add adilkalam/orca --skill ascii-tables
Clone the repo
git clone --depth 1 https://github.com/adilkalam/orca

Made for: Claude Code, Codex.

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 table

README.md
[![agentmods](https://agentmods.dev/badge/skills/adilkalam/orca/ascii-tables/github.svg)](https://agentmods.dev/skills/adilkalam/orca/ascii-tables)
Your own site
<a href="https://agentmods.dev/skills/adilkalam/orca/ascii-tables"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/ascii-tables/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.

agentmods 80×15 button for table

Your own site · 80×15
<a href="https://agentmods.dev/skills/adilkalam/orca/ascii-tables"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/ascii-tables.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 13 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,444 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00013 $0.01444
Opus 5 $0.00006 $0.00722
Sonnet 5 $0.00003 $0.00289
Haiku 4.5 $0.00001 $0.00144

Measured 9d ago against content hash 470e7954cc51, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

table 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 9d 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/ascii-tables/SKILL.md · 255 lines

How it starts

The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ASCII Table Alignment Skill

THE PROBLEM

LLMs cannot align markdown tables correctly because:

  1. Token-by-token generation - No spatial awareness during output
  2. Variable character widths - Even in monospace fonts:
    • CJK characters = 2 cells wide
    • Emoji = variable width
    • Combining characters = 0 width
  3. No backtracking - Cannot adjust previous output based on later content

No amount of prompt engineering fixes this. The problem is fundamental to how LLMs generate text.


THE SOLUTION

Two-phase approach:

  1. Generation Phase (LLM) - Focus on content, not alignment
  2. Formatting Phase (Script) - wcwidth-based column alignment

MANDATORY PROTOCOL

When generating markdown tables, you MUST follow this protocol:

Step 1: Generate Table Content

Focus on correctness and completeness. Do NOT waste effort on manual alignment.

| Column A | Column B | Column C |
|---|---|---|
| Short | Much longer content here | X |
| Another row | Data | More data |

Step 2: Run Formatter (MANDATORY)

After generating any markdown table, you MUST run:

python3 ~/.claude/scripts/md-table-formatter.py /path/to/file.md

Or for stdin/stdout:

echo "table content" | python3 ~/.claude/scripts/md-table-formatter.py

Step 3: Verify Output

Check stderr for verification report:

TABLE_FORMAT_CHECK:
- Tables processed: N
- Column widths: [W1, W2, W3, ...]
- Status: ALIGNED

GENERATION GUIDELINES

When creating table content:

  1. Use simple separators - |---|---|---| not |:---:|:---:|:---:|
  2. Content first - Get the data right, formatting comes later
  3. One table at a time - Easier to verify
  4. Preserve semantics - Formatter only adjusts spacing

What the Formatter Does

  • Calculates display width of each cell using wcwidth
  • Finds maximum width for each column
  • Pads cells with spaces to align columns
  • Normalizes separator rows

Read the full file on GitHub · 255 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. 9d ago First seen · 255 lines · 13 tokens per session scan A 470e7954cc51

Subscribe to this mod's changes

table is a skill published in the GitHub repository adilkalam/orca (2 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 1,444 once invoked, about $0.0001 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

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens