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.
npx agentmods add agents/sareegpt/edgartools-mcp/content-creatorgit clone --depth 1 https://github.com/sareegpt/edgartools-mcpWrote 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.
[](https://agentmods.dev/agents/sareegpt/edgartools-mcp/content-creator)<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>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.
| Model | Per session | Once 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 |
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) 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:
- Identify which edgartools features the content will cover (e.g., insider trades -> ownership, financial statements -> financials)
- 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, metricsreports/skill.yaml-- if content involves 10-K, 10-Q, 8-K report sectionsholdings/skill.yaml-- if content involves 13F institutional holdingsownership/skill.yaml-- if content involves Form 4 insider transactionsxbrl/skill.yaml-- if content involves XBRL data, facts, taxonomy
- Read
docs/internal/docs-guidelines.mdfor documentation formatting standards - 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:
- Read skill files -- load the relevant skill YAMLs to learn the correct API patterns (see above)
- Write and test code -- run edgartools code to verify it works and produces visually appealing output
- Capture screenshots -- use
scripts/snapshot_rich.pyto capture rich terminal output as WebP images - Optimize images -- ensure all images are WebP format, stored in
docs/images/with descriptive names - Write the article -- follow the project's documentation templates and standards
- 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 usedocs/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)
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.
- 5d ago First seen · 172 lines · 0 tokens per session scan A e81347caa819
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.
Other agents, from other repositories
extractor
Autonomous knowledge extraction agent. Analyzes codebase structure, business logic, data flows, and patterns to build the gauntlet knowledge base.
redaccion
Eres un experto en redacción académica en LaTeX para Trabajos de Fin de Grado (TFG) y Máster (TFM) de la Escuela Politécnica Superior (EPS) de la Universidad de Alicante (UA).
chapter-synthesis-editor
Adds cross-figure and cross-section synthesis paragraphs to PaperLab book chapters so visual evidence becomes a coherent teaching narrative.
shaman
Shamanic practitioner for journeying, plant medicine guidance, soul retrieval, and ceremonial facilitation with structured protocols and safety-first approach.
slide-auditor
Visual layout auditor for RevealJS and Beamer slides. Checks for overflow, font consistency, box fatigue, and spacing issues. Use proactively after creating or modifying slides.
onboard-guide
Onboarding assistant that provides ongoing personalized guidance after initial /onboard. Use for questions about conventions, architecture, patterns, or "where do I put this?" — answers are tailored to the engineer's background.