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 zubair-trabzada/dataforseo-claude --skill seo-report-pdfgit clone --depth 1 https://github.com/zubair-trabzada/dataforseo-claudeWrote 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/zubair-trabzada/dataforseo-claude/seo-report-pdf)<a href="https://agentmods.dev/skills/zubair-trabzada/dataforseo-claude/seo-report-pdf"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/dataforseo-claude/seo-report-pdf/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/zubair-trabzada/dataforseo-claude/seo-report-pdf"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/dataforseo-claude/seo-report-pdf.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00051 | $0.00814 |
| Opus 5 | $0.00026 | $0.00407 |
| Sonnet 5 | $0.00010 | $0.00163 |
| Haiku 4.5 | $0.00005 | $0.00081 |
Grade A, and why
seo-report-pdf 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 12d 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.
Asks for rootlowPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
3. `chmod 600 ~/.claude/skills/seo/.env` Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phase 0: Credential Preflight (REQUIRED — run BEFORE anything else)
Before running any of the steps below, always invoke the shared preflight check:
~/.claude/skills/seo/scripts/preflight.sh
If exit code is 0: credentials are configured — proceed with the rest of this skill silently.
If exit code is 2: the script prints the DataForSEO setup wizard to stdout. STOP, display that wizard to the user verbatim, and wait for them to paste credentials in this format:
login: [email protected]
password: their_api_password_here
When they reply:
- Parse
login:andpassword:from their message. - Write them to
~/.claude/skills/seo/.env:DATAFORSEO_LOGIN=<login> DATAFORSEO_PASSWORD=<password> chmod 600 ~/.claude/skills/seo/.env- Run a verification call:
~/.claude/skills/seo/scripts/keyword_research.py volume "test" - If verification succeeds (real JSON returned): tell the user "✅ Credentials verified. Running your command now..." and proceed with the original request.
- If status
40104 — Please verify your account: tell the user to verify their account at https://app.dataforseo.com/, then say "continue" to retry. - If any other auth error: ask them to double-check the API password (the long alphanumeric string from https://app.dataforseo.com/api-access — not their account login password).
Never echo credentials back to the user, never include them in tool output, and never commit them.
SEO PDF Report Skill
Powered by: Reads the audit JSON produced by
/seo audit, built from live DataForSEO API data. PDF generation uses ReportLab — no additional API calls.
Prerequisite
/seo audit <domain> must have run first and saved
~/.claude/skills/seo/output/<domain>-audit.json. If that file doesn't
exist, tell the user to run the audit first.
Run
~/.claude/skills/seo/scripts/generate_pdf_report.py \
--input ~/.claude/skills/seo/output/<domain>-audit.json \
--output ~/.claude/skills/seo/output/<domain>-report.pdf
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.
- 12d ago First seen · 94 lines · 51 tokens per session scan A 633b68f438ba
seo-report-pdf is a skill published in the GitHub repository zubair-trabzada/dataforseo-claude (154 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 814 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
pydicom
Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
skill-doc-delivery
Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.
pdf-extract-create-workflow
Complete PDF lifecycle: download, extract, and generate structured documents with reportlab.
document-direct-python
Use direct Python execution for reliable document creation including spreadsheets, PDFs, and structured reports.
parse-document
Convert a PDF, scan, image of a page, or office file to clean markdown through the connected Superlinked MCP edge, so the source document is not read into model context directly. Use when the user asks to read, parse, OCR, extract from, summarize, or answer questions about a document.