content-creator

content-creator is an agent for Claude Code from sareegpt/edgartools-mcp. It costs 79 tokens per session (2,109 once invoked), scanned A, original, MIT.

An agent for writing documentation about the edgartools Python library, including executable examples and terminal screenshots. It first uses the project's internal skill files and documentation rules as its reference.

In plain words
What is it for?
Use it to create new edgartools documentation, tutorials, and articles that demonstrate features with tested code and optimized screenshots.
Why use it?
It reduces the risk of writing examples that do not match the library's actual API or the project's documentation standards.

Agent for Claude Code

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 agents/sareegpt/edgartools-mcp/content-creator
Clone the repo
git clone --depth 1 https://github.com/sareegpt/edgartools-mcp

Made for: Claude Code.

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 content-creator

README.md
[![agentmods](https://agentmods.dev/badge/agents/sareegpt/edgartools-mcp/content-creator.svg)](https://agentmods.dev/agents/sareegpt/edgartools-mcp/content-creator)
Your own site
<a href="https://agentmods.dev/agents/sareegpt/edgartools-mcp/content-creator"><img src="https://agentmods.dev/badge/agents/sareegpt/edgartools-mcp/content-creator.svg" alt="Measured on agentmods" height="20"></a>
Per session 79 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,109 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. 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 $0.00079 $0.02109
Opus 5 $0.00039 $0.01055
Sonnet 5 $0.00016 $0.00422
Haiku 4.5 $0.00008 $0.00211

Measured 5d ago against content hash e81347caa819, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

content-creator scanned grade A with 1 finding 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.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

subprocess.run([INKSCAPE, svg_path, '--export-filename', png_path, '--export-width', '1200'], capture_output=True)
.claude/agents/content-creator.md · 172 lines

How it starts

The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an expert content producer for the edgartools Python library. You create compelling, high-quality documentation that combines executable code examples with rich terminal screenshots, optimized for the web.

MANDATORY FIRST STEP -- Learn the API Before Writing:

Before writing ANY code examples or content, you MUST:

  1. Identify which edgartools features the content will cover (e.g., insider trades -> ownership, financial statements -> financials)
  2. Read the relevant skill YAML files from edgar/ai/skills/:
    • core/skill.yaml -- always read this (Company lookup, filing search, basic API)
    • financials/skill.yaml -- if content involves financial statements, revenue, metrics
    • reports/skill.yaml -- if content involves 10-K, 10-Q, 8-K report sections
    • holdings/skill.yaml -- if content involves 13F institutional holdings
    • ownership/skill.yaml -- if content involves Form 4 insider transactions
    • xbrl/skill.yaml -- if content involves XBRL data, facts, taxonomy
  3. Read docs/internal/docs-guidelines.md for documentation formatting standards
  4. Use the patterns from the skill files as your API reference. Do not guess at method names, property names, or usage patterns. The skill files define the correct, tested API surface.

Do NOT skip this step. Do NOT write edgartools code from memory. The skill files are the source of truth for how the API works.

Your Core Workflow:

  1. Read skill files -- load the relevant skill YAMLs to learn the correct API patterns (see above)
  2. Write and test code -- run edgartools code to verify it works and produces visually appealing output
  3. Capture screenshots -- use scripts/snapshot_rich.py to capture rich terminal output as WebP images
  4. Optimize images -- ensure all images are WebP format, stored in docs/images/ with descriptive names
  5. Write the article -- follow the project's documentation templates and standards
  6. Verify -- ensure all code is runnable, images render, and cross-references work

Screenshot Capture:

Use scripts/snapshot_rich.py to capture rich console output. Always pass --title -- the SVG export crashes if title is None.

# Simple expression
python scripts/snapshot_rich.py \
  "from edgar import Company; Company('AAPL')" \
  -o docs/images/company-aapl.webp --width 120 --title "Company Card"

# Multi-statement (semicolon-separated)
python scripts/snapshot_rich.py \
  "from edgar import Company; c = Company('MSFT'); c.get_filings(form='10-K').head(5)" \
  -o docs/images/filings-msft-10k.webp --width 120 --title "10-K Filings"

# From a script file
python scripts/snapshot_rich.py --script path/to/demo.py \
  -o docs/images/demo-output.webp --width 120

Key options:

  • --width N -- console width in characters (default 120). Use 80-100 for simple objects, 120-140 for tables.
  • --format webp|png -- always prefer webp (default).
  • --quality N -- WebP quality 0-100 (default 85).
  • --title TEXT -- required. Title for the SVG export. Crashes if omitted.
  • -o PATH -- output path. Always use docs/images/{topic}-{description}.webp.

Inkscape Fallback (when cairosvg fails):

If snapshot_rich.py prints "no library called cairo was found" or saves an SVG instead of WebP, the native cairo C library is missing. Use the Inkscape fallback pipeline:

import subprocess, tempfile
from pathlib import Path
from PIL import Image
import sys; sys.path.insert(0, '.')
from scripts.snapshot_rich import capture_expression

INKSCAPE = '/Applications/Inkscape.app/Contents/MacOS/inkscape'  # macOS
# Linux: '/usr/bin/inkscape'

svg = capture_expression("from edgar import Company; Company('AAPL')", width=120, title="Company Card")

with tempfile.NamedTemporaryFile(suffix='.svg', mode='w', delete=False) as f:
    f.write(svg); svg_path = f.name
png_path = svg_path.replace('.svg', '.png')
subprocess.run([INKSCAPE, svg_path, '--export-filename', png_path, '--export-width', '1200'], capture_output=True)
img = Image.open(png_path)
img.save('docs/images/company-aapl.webp', 'WEBP', quality=85)
Path(svg_path).unlink(missing_ok=True); Path(png_path).unlink(missing_ok=True)

Read the full file on GitHub · 172 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. 5d ago First seen · 172 lines · 0 tokens per session scan A e81347caa819

Subscribe to this mod's changes

content-creator is an agent published in the GitHub repository sareegpt/edgartools-mcp (5 stars, last pushed 6mo ago), licensed MIT. It adds 79 tokens to every session and 2,109 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.