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 skills add bobmatnyc/claude-mpm-skills --skill sec-edgar-pipelinegit clone --depth 1 https://github.com/bobmatnyc/claude-mpm-skillsWrote 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/skills/bobmatnyc/claude-mpm-skills/sec-edgar-pipeline)<a href="https://agentmods.dev/skills/bobmatnyc/claude-mpm-skills/sec-edgar-pipeline"><img src="https://agentmods.dev/badge/skills/bobmatnyc/claude-mpm-skills/sec-edgar-pipeline/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.
<a href="https://agentmods.dev/skills/bobmatnyc/claude-mpm-skills/sec-edgar-pipeline"><img src="https://agentmods.dev/badge/skills/bobmatnyc/claude-mpm-skills/sec-edgar-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00028 | $0.00808 |
| Opus 5 | $0.00014 | $0.00404 |
| Sonnet 5 | $0.00006 | $0.00162 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
sec-edgar-pipeline 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.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEC EDGAR Pipeline
Overview
This pipeline is centered on edgar-analyzer and the EDGAR data sources. The core loop is: configure credentials, create a project with examples, analyze patterns, generate code, run extraction, and export reports.
Setup (Keys + User Agent)
Use the setup wizard to configure required keys:
python -m edgar_analyzer setup
# or
edgar-analyzer setup
Required entries:
OPENROUTER_API_KEY- (Optional)
JINA_API_KEY EDGARuser agent string ("Name [email protected]")
End-to-End CLI Workflow
# 1. Create project
edgar-analyzer project create my_project --template minimal
# 2. Add examples + project.yaml
# projects/my_project/examples/*.json
# 3. Analyze examples
edgar-analyzer analyze-project projects/my_project
# 4. Generate extraction code
edgar-analyzer generate-code projects/my_project
# 5. Run extraction
edgar-analyzer run-extraction projects/my_project --output-format csv
Outputs land in projects/<name>/output/.
EDGAR-Specific Conventions
- CIK values are 10-digit, zero-padded (e.g.,
0000320193). - Rate limit: SEC API allows 10 requests/sec. Scripts use ~0.11s delays.
- User agent is mandatory; include name + email.
Scripted Example (Apple DEF 14A)
edgar/scripts/fetch_apple_def14a.py shows the direct flow:
- Fetch latest DEF 14A metadata
- Download HTML
- Parse Summary Compensation Table (SCT)
- Save raw HTML + extracted JSON + ground truth
Recipe-Driven Extraction
edgar/recipes/sct_extraction/config.yaml defines a multi-step pipeline:
- Fetch DEF 14A filings by company list
- Extract SCT tables with
SCTAdapter - Validate with
sct_validator - Write results to
output/sct
Report Generation
edgar/scripts/create_csv_reports.py converts JSON results into:
executive_compensation_<timestamp>.csvtop_25_executives_<timestamp>.csvcompany_summary_<timestamp>.csv
Troubleshooting
- No filings found: confirm CIK formatting and filing type (DEF 14A vs DEF 14A/A).
- API errors: slow down requests and confirm user-agent is set.
- Extraction errors: regenerate code or use manual ground truth in POC scripts.
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.
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.
- 9d ago First seen · 109 lines · 28 tokens per session scan A e69bb1d60a11
sec-edgar-pipeline is a skill published in the GitHub repository bobmatnyc/claude-mpm-skills (75 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 808 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 2026-09-03.
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