Agent skill

Agency Report PDF

by zubair-trabzada in zubair-trabzada/ai-agency-claude

Unified PDF report generator — combines all audit scores into a professional client-ready PDF

MITAuto-check passedDocuments & Office

Install Agency Report PDF

skills CLI
$ npx skills add zubair-trabzada/ai-agency-claude --skill agency-report-pdf -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zubair-trabzada/ai-agency-claude agency-report-pdf --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zubair-trabzada/ai-agency-claude.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agency-report-pdf .claude/skills/agency-report-pdf && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agency-report-pdf
GitHub stars
151
Token cost
~4.2k tokens
SKILL.md length
1,253 words
Files
1
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

Unified PDF report generator — combines all audit scores into a professional client-ready PDF

  • Works in 8 steps: Scan for Available Audit Files → Extract Data from Each Audit File → Calculate Composite Scores (if not… → …
  • Tasks that involve PDF
  • SKILL.md covers Trigger, Overview of the PDF Generation…, Step 1 — Scan for Available… and Step 2 — Extract Data from…, plus 9 more sections
  • Calls python3 and pip3

What it does

Agency Report PDF is an agent skill from zubair-trabzada/ai-agency-claude. Unified PDF report generator — combines all audit scores into a professional client-ready PDF

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Documents & Office, covering PDF. The repository describes itself as: AI Agency Command Center for Claude Code — orchestrates 5 AI teams (Marketing, Sales, Legal, Reputation, GEO/SEO) into a unified zero-employee agency. 9 skills, 5 parallel… The licence is MIT.

When your agent uses it

  • Tasks that involve PDF

Example prompts

  • “/agency-report-pdf”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Scan for Available Audit Files
  2. Extract Data from Each Audit File
  3. Calculate Composite Scores (if not already available)
  4. Determine Service Tier Recommendation
  5. Build the JSON Data Structure
  6. Write the JSON File
  7. Run the PDF Generation Script
  8. Confirm Output

What it can do on your machine

Read from SKILL.md and the folder at commit 172a6c2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3
    • pip3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip3, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agency Report PDF loads about 4.2k tokens when it runs. Until then it costs about 28 tokens; SKILL.md has 1,253 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~28
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zubair-trabzada/ai-agency-claude at commit 172a6c2, republished under its MIT licence (© zubair-trabzada). 1,253 words, ~4,188 tokens.

Download SKILL.mdSave it as .claude/skills/agency-report-pdf/SKILL.md (or your agent's skills folder).
name
agency-report-pdf
description
Unified PDF report generator — combines all audit scores into a professional client-ready PDF

Unified Agency PDF Report Generator

You are the PDF Report Generator for the AI Agency Command Center. When the user runs /agency report-pdf, you scan the current directory for all audit output files, extract scores and findings from each available audit, prepare a structured JSON data file, and run the Python PDF generation script to produce a professional, multi-page AGENCY-REPORT.pdf.

Trigger

This skill activates when the user runs:

/agency report-pdf

No arguments required. This command operates on whatever audit files exist in the current working directory.

Overview of the PDF Generation Pipeline

[Scan Directory] → [Extract Data from Audit Files] → [Build JSON Structure] → [Write agency_data.json] → [Run Python Script] → [AGENCY-REPORT.pdf]

The Python script at ~/.claude/skills/agency/scripts/generate_agency_pdf.py handles all PDF rendering. Your job is to prepare the data. The script expects a file called agency_data.json in the current working directory.

Step 1 — Scan for Available Audit Files

Search the current working directory for all audit output files using Glob. Check for each of these file patterns:

Agency-Level Files
AGENCY-ONBOARD-*.md       → Primary source for composite scores
AGENCY-PROPOSAL-*.md      → Proposal data for service recommendations
Individual Tool Suite Files
MARKETING-AUDIT*.md       → Marketing score and findings
REPUTATION-AUDIT-*.md     → Reputation score and findings
GEO-AUDIT-*.md            → GEO/SEO score and findings
LEGAL-COMPLIANCE-*.md     → Legal score and findings
PROSPECT-ANALYSIS*.md     → Sales/opportunity score and findings
SALES-RESEARCH*.md        → Additional sales data
Supplementary Files (for enrichment)
REPUTATION-REVIEWS*.md    → Review data for reputation section
REPUTATION-SENTIMENT*.md  → Sentiment data
GEO-CITABILITY*.md        → Citability details
GEO-SCHEMA*.md            → Schema markup details
GEO-CRAWLERS*.md          → Crawler access data
MARKETING-SEO*.md         → SEO detail data
MARKETING-FUNNEL*.md      → Funnel data
LEGAL-PRIVACY*.md         → Privacy policy details
LEGAL-TERMS*.md           → Terms of service details

If NO audit files are found at all, display an error:

No audit files found in the current directory.
Run /agency onboard <url> first to generate audit data, then try again.

Step 2 — Extract Data from Each Audit File

Read each discovered file and extract the relevant data points. Use careful parsing — scores may appear in different formats across files.

2A — Extract from Agency Onboard Report (AGENCY-ONBOARD-*.md)

This is the richest data source. If present, it contains everything. Look for:

  • Company name — Usually in the title or first heading
  • Agency Score — Look for patterns like "Agency Score: XX/100", "Composite Score: XX", or a score table
  • Agency Grade — Look for "Grade: X" or grade in the score table
  • Individual scores — Look for a score breakdown table or section with:
    • Marketing Score (or Marketing: XX/100)
    • Reputation Score
    • GEO Score (or GEO/SEO Score)
    • Legal Score
    • Sales Score (or Opportunity Score)
  • Critical findings — Look for sections titled "Critical Findings", "Key Issues", or "Problems Found". Extract the top 3 from each team.
  • Quick wins — Look for sections titled "Quick Wins", "Easy Fixes", or "Low-Hanging Fruit". Extract the top 3 from each team.
  • Recommended service tier — Look for "Recommended", "Service Package", "Pricing", or tier names (Essentials, Growth, Full Agency)
  • 90-day action plan — Look for phased roadmap, timeline, or action plan sections
  • Company profile data — Industry, location, business type, website URL
2B — Extract from Individual Marketing Audit (MARKETING-AUDIT*.md)

If no agency onboard exists, or to supplement it:

  • Marketing Score — Look for "Marketing Score: XX/100", "Overall Score: XX", or similar
  • Copy quality assessment — Rating or description of website copy
  • SEO status — Meta tags, headings, content structure assessment
  • Conversion elements — CTAs, forms, social proof evaluation
  • Content strategy — Blog presence, thought leadership assessment
  • Critical findings — Top 3 marketing issues
  • Quick wins — Top 3 easy marketing fixes
  • Recommended marketing services — With pricing if available
2C — Extract from Reputation Audit (REPUTATION-AUDIT-*.md)
  • Reputation Score — Look for "Reputation Score: XX/100" or similar
  • Google rating — Star rating (e.g., 3.8/5.0)
  • Review count — Total number of Google reviews
  • Sentiment breakdown — Positive/negative/neutral percentages
  • Response rate — Percentage of negative reviews with owner responses
  • Competitor comparison — How this business compares to local competitors
  • Critical findings — Top 3 reputation issues
  • Quick wins — Top 3 easy reputation fixes
2D — Extract from GEO Audit (GEO-AUDIT-*.md)
  • GEO Score — Look for "GEO Score: XX/100" or "AI Visibility Score"
  • Citability Score — How likely AI systems cite this content
  • AI crawler access — Which AI crawlers are allowed/blocked
  • Schema markup status — Present, partial, or missing
  • Platform readiness — Scores for ChatGPT, Perplexity, Gemini, Google AI Overviews
  • Critical findings — Top 3 GEO/SEO issues
  • Quick wins — Top 3 easy GEO fixes
  • Legal Score — Look for "Legal Score: XX/100" or "Compliance Score"
  • Privacy policy status — Present/missing, compliant/non-compliant
  • Terms of service status — Present/missing, issues found
  • Cookie consent — Compliant/non-compliant
  • ADA/accessibility — Status and issues
  • Critical findings — Top 3 compliance gaps
  • Quick wins — Top 3 easy compliance fixes
2F — Extract from Sales/Prospect Analysis (PROSPECT-ANALYSIS*.md)
  • Sales Score — Look for "Opportunity Score: XX/100" or "Sales Score"
  • Company size — Employee count, revenue estimates
  • Industry — Business category
  • Decision makers — Names, titles, contact strategies
  • Budget capacity — Estimated budget
  • Critical findings — Top 3 sales insights
  • Quick wins — Top 3 engagement opportunities

Step 3 — Calculate Composite Scores (if not already available)

If the agency onboard file is present and has a composite score, use it directly.

If individual scores exist but no composite, calculate:

Agency Score = (Marketing x 0.25) + (Reputation x 0.20) + (GEO x 0.20) + (Legal x 0.15) + (Sales x 0.20)

If some scores are missing, recalculate weights proportionally across available scores. For example, if only Marketing (25%), Reputation (20%), and GEO (20%) are available:

Total available weight = 0.25 + 0.20 + 0.20 = 0.65
Adjusted: Marketing = 0.25/0.65, Reputation = 0.20/0.65, GEO = 0.20/0.65
Grade Assignment
ScoreGrade
85-100A+
70-84A
55-69B
40-54C
25-39D
0-24F
Show full SKILL.md (548 more words)Show less

Step 4 — Determine Service Tier Recommendation

Based on the composite score and number of critical findings:

Tier 1 — Essentials ($500-$1,500/month)

  • Agency Score 55+ (Grade B or better)
  • Fewer than 8 critical findings total
  • Focus: monitoring, basic fixes, maintenance

Tier 2 — Growth ($1,500-$3,500/month)

  • Agency Score 35-54 (Grade C-D)
  • 8-15 critical findings total
  • Focus: active improvement across multiple dimensions

Tier 3 — Full Agency ($3,500-$7,500/month)

  • Agency Score below 35 (Grade D-F)
  • 15+ critical findings total
  • Focus: complete overhaul and ongoing management

If a proposal file exists, use the pricing from the proposal instead of estimating.

Step 5 — Build the JSON Data Structure

Construct the following JSON structure. All fields are required. Use null for unavailable data, never omit keys.

json
{
  "company_name": "Business Name",
  "date": "2026-04-05",
  "website_url": "https://example.com",
  "industry": "Industry category",
  "location": "City, State",

  "agency_score": 52,
  "agency_grade": "C",

  "marketing_score": 45,
  "reputation_score": 62,
  "geo_score": 38,
  "legal_score": 55,
  "sales_score": 68,

  "scores_available": {
    "marketing": true,
    "reputation": true,
    "geo": true,
    "legal": true,
    "sales": true
  },

  "marketing_findings": {
    "critical": [
      "No clear value proposition above the fold",
      "Missing meta descriptions on 80% of pages",
      "No email capture or lead magnet anywhere on site"
    ],
    "quick_wins": [
      "Add a compelling headline with specific benefit to homepage",
      "Write unique meta descriptions for top 10 pages",
      "Add a simple email signup with a free guide offer"
    ],
    "summary": "Website copy is generic and lacks conversion elements. SEO foundations are weak with missing meta data across most pages."
  },

  "reputation_findings": {
    "critical": [
      "3.2 star rating with only 12 Google reviews",
      "Zero responses to negative reviews",
      "Competitors average 4.5 stars with 50+ reviews"
    ],
    "quick_wins": [
      "Respond to all negative reviews within 48 hours",
      "Set up an automated review request sequence",
      "Create a Google review link and add to email signatures"
    ],
    "summary": "Reputation is below industry average. Low review volume and no engagement with negative feedback are the primary concerns.",
    "google_rating": 3.2,
    "review_count": 12,
    "response_rate": 0
  },

  "geo_findings": {
    "critical": [
      "AI crawlers blocked by restrictive robots.txt",
      "No structured data/schema markup on any page",
      "Content not formatted for AI citation"
    ],
    "quick_wins": [
      "Update robots.txt to allow GPTBot and ClaudeBot",
      "Add LocalBusiness schema to homepage",
      "Add FAQ schema to service pages"
    ],
    "summary": "Site is invisible to AI search engines. Blocked crawlers and missing schema mean zero AI-driven traffic.",
    "citability_score": null,
    "crawler_access": "blocked"
  },

  "legal_findings": {
    "critical": [
      "No privacy policy found on website",
      "Cookie tracking active without consent mechanism",
      "No terms of service"
    ],
    "quick_wins": [
      "Add a basic privacy policy using a template generator",
      "Install a cookie consent banner",
      "Add terms of service page"
    ],
    "summary": "Website has significant compliance gaps. Missing privacy policy and terms expose the business to legal risk."
  },

  "sales_findings": {
    "critical": [
      "No clear decision maker identified from public data",
      "Company shows signs of budget constraints",
      "Competitive market with established agencies already serving them"
    ],
    "quick_wins": [
      "Connect on LinkedIn with the business owner",
      "Lead with the free reputation audit as conversation starter",
      "Reference specific negative reviews in outreach"
    ],
    "summary": "Moderate sales opportunity. Owner-operated business with clear pain points but budget may be limited.",
    "company_size": "Small (5-10 employees)",
    "decision_makers": []
  },

  "recommended_tier": {
    "name": "Growth",
    "tier_number": 2,
    "monthly_price_low": 1500,
    "monthly_price_high": 3500,
    "services": [
      "Marketing optimization and content strategy",
      "Reputation management with review responses",
      "GEO/SEO implementation",
      "Monthly reporting across all dimensions",
      "Quarterly strategy calls"
    ]
  },

  "action_plan": {
    "month_1": [
      "Fix critical compliance gaps (privacy policy, cookie consent)",
      "Update robots.txt for AI crawler access",
      "Respond to all existing negative reviews",
      "Rewrite homepage headline and value proposition"
    ],
    "month_2": [
      "Implement schema markup on all key pages",
      "Launch review request campaign targeting recent customers",
      "Create 4 blog posts targeting top industry keywords",
      "Set up email capture with lead magnet"
    ],
    "month_3": [
      "Full content audit and optimization for AI citability",
      "Competitive analysis refresh and positioning update",
      "Build comprehensive FAQ section for AI search visibility",
      "First monthly progress report with score comparisons"
    ]
  },

  "source_files": [
    "AGENCY-ONBOARD-CompanyName.md",
    "REPUTATION-AUDIT-CompanyName.md",
    "GEO-AUDIT-CompanyName.md"
  ]
}

Step 6 — Write the JSON File

Write the constructed JSON to agency_data.json in the current working directory:

Use the Write tool to create agency_data.json with the full JSON structure

Validate the JSON is well-formed before writing. Ensure:

  • All scores are integers 0-100 or null
  • All arrays have at most 4 items (to fit PDF layout)
  • All strings are properly escaped
  • The date is in YYYY-MM-DD format
  • No trailing commas

Step 7 — Run the PDF Generation Script

Execute the Python PDF generator:

bash
python3 ~/.claude/skills/agency/scripts/generate_agency_pdf.py

The script reads agency_data.json from the current directory and outputs AGENCY-REPORT.pdf to the current directory.

If the Script Fails
  1. Script not found — Inform the user:

    PDF generation script not found at ~/.claude/skills/agency/scripts/generate_agency_pdf.py
    The agency_data.json has been prepared. You can generate the PDF once the script is installed.
  2. Python dependency missing — The script requires reportlab. If the import fails:

    bash
    pip3 install reportlab

    Then retry the script.

  3. JSON parsing error — Re-validate the JSON structure. Common issues:

    • Unescaped quotes in finding text
    • Null values where strings are expected
    • Missing required fields
  4. Other errors — Display the full error output and suggest the user check the script.

Step 8 — Confirm Output

After successful PDF generation, display:

================================================================
  AGENCY REPORT PDF GENERATED
================================================================

  File:     AGENCY-REPORT.pdf
  Client:   [Company Name]
  Date:     [Date]
  Score:    [Agency Score]/100 (Grade [Grade])
  Pages:    [Estimated page count based on data]

  Scores included:
    Marketing:     [score or "N/A"]
    Reputation:    [score or "N/A"]
    GEO/SEO:       [score or "N/A"]
    Legal:         [score or "N/A"]
    Sales:         [score or "N/A"]

  Data source: agency_data.json

  The PDF has been saved to the current directory.
  Share it with your client as a professional audit summary.
================================================================

Handling Partial Data

Not all 5 audits need to be present. The report adapts to whatever data is available:

  • Only 1 audit available — Generate a single-dimension report. Note which audits are missing and recommend running them.
  • 2-4 audits available — Generate a partial composite score using proportional weights. Clearly mark which dimensions were not assessed.
  • All 5 audits available — Full comprehensive report.

For missing dimensions, the JSON should use null for the score and empty arrays for findings:

json
{
  "legal_score": null,
  "legal_findings": {
    "critical": [],
    "quick_wins": [],
    "summary": "Legal compliance audit not yet performed."
  }
}

Data Quality Rules

  1. Never fabricate scores — Only include scores actually found in audit files. Use null for missing data.
  2. Preserve original wording — Copy findings verbatim from audit files. Do not rephrase or embellish.
  3. Trim to fit — Each findings array should have exactly 3-4 items max. If the audit has more, pick the highest-impact ones.
  4. Validate score ranges — Scores must be 0-100 integers. If a file has a score outside this range, cap it.
  5. Date accuracy — Use the date from the most recent audit file, not today's date, unless today is the audit date.

Multiple Clients in Directory

If the current directory contains audit files for multiple businesses:

  1. Identify all unique business names from file names
  2. Ask the user which client the report should be for
  3. Filter to only that client's files
  4. If the user says "all" — generate one report for the most recently audited client and note others are available

Do NOT silently merge data from different businesses into one report.

© zubair-trabzada, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/agency-report-pdf of zubair-trabzada/ai-agency-claude.

Open the folder on GitHubat commit 172a6c2

Compare with similar skills

Agency Report PDF next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agency Report PDF compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agency Report PDF this skillzubair-trabzada/ai-agency-claude151—~4.2kAutomated safety check: PassMIT
MarkitdownImCa0/just-laws78114 repos~3.2kAutomated safety check: NotesMIT
Gzh Designisjiamu/gzh-design-skill4k—~2.2kAutomated safety check: PassAGPL-3.0
GenOffice Document CLIgenspark-ai/genoffice9.2k—~19kAutomated safety check: PassApache-2.0
Harness Book Best Practicewquguru/harness-books3.2k—~4.1kAutomated safety check: PassNone
Bookforge Korean Ebook PDF Makergongnyang/bookforge3161 repos~1.7kAutomated safety check: PassMIT

Similar skills

  • Markitdown

    ImCa0/just-laws

    Convert files and office documents to Markdown. An agent skill from ImCa0/just-laws.

    781 GitHub starsUsed in 14 repos~3.2k tokens
    Documents & OfficeAuto-check: notes
  • Gzh Design

    isjiamu/gzh-design-skill

    微信公众号文章排版引擎,将 Markdown 转换为可直接粘贴到公众号编辑器的 HTML。主题风格从 references/theme-index.md 注册的自定义主题库中选取,自动章节编号、关键词下划线标记、引言卡片、目录导航、代码块、图片/GIF、作者签名。支持 Markdown / Word(.docx) / PDF / 纯文本输入(非 Markdown…

    4k GitHub stars~2.2k tokensUpdated 2 days ago
    Documents & OfficeAuto-check passed
  • GenOffice Document CLI

    genspark-ai/genoffice

    Creates, converts, reads and edits real pptx, xlsx, docx and PDF files locally through the genoffice command line.

    9.2k GitHub stars~19k tokensUpdated yesterday
    Documents & OfficeAuto-check passed
  • Harness Book Best Practice

    wquguru/harness-books

    Best practices for working on the Harness books repo. An agent skill from wquguru/harness-books.

    3.2k GitHub stars~4.1k tokensUpdated 5 mo ago
    Documents & OfficeAuto-check passed
  • Produces book-style Korean ebook PDFs from a topic or finished manuscript, with six design styles, real book parts and quality-check gates before output.

    316 GitHub starsUsed in 1 repo~1.7k tokens
    Documents & OfficeAuto-check passed
  • Instrument Data To Allotrope

    aws-samples/amazon-bedrock-agents-healthcare-lifesciences

    Official

    Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV.

    274 GitHub starsUsed in 2 repos~2.7k tokens
    Documents & OfficeAuto-check passed

More from zubair-trabzada/ai-agency-claude

  • Agency Client

    zubair-trabzada/ai-agency-claude

    Client lookup and history — searches all output files for a client and displays a comprehensive summary

    151 GitHub stars~3.2k tokensUpdated 6 mo ago
    Auto-check passed
  • Agency Pipeline

    zubair-trabzada/ai-agency-claude

    Prospect Pipeline Manager — scans all audit files to build a scored, staged pipeline view with revenue projections

    151 GitHub stars~3.3k tokensUpdated 6 mo ago
    Auto-check passed
  • Agency Propose

    zubair-trabzada/ai-agency-claude

    Unified Agency Proposal Generator — builds a three-tier service proposal with ROI projections from all available audit data

    151 GitHub stars~4.5k tokensUpdated 6 mo ago
    Auto-check passed
  • Agency Quick

    zubair-trabzada/ai-agency-claude

    60-Second Agency Snapshot — rapid 5-dimension assessment without subagents, outputs a compact scorecard

    151 GitHub stars~4.2k tokensUpdated 6 mo ago
    Auto-check passed
  • Agency Status

    zubair-trabzada/ai-agency-claude

    Agency dashboard — shows agency-wide status, pipeline metrics, installed tools, and recent activity

    151 GitHub stars~2.9k tokensUpdated 6 mo ago
    Auto-check passed

Questions about Agency Report PDF

What does Agency Report PDF do?

Unified PDF report generator — combines all audit scores into a professional client-ready PDF. Agency Report PDF is an agent skill from zubair-trabzada/ai-agency-claude.

When should I use Agency Report PDF?

Agency Report PDF fits situations like: tasks that involve PDF.

How do I install Agency Report PDF in Claude Code?

Run `npx skills add zubair-trabzada/ai-agency-claude --skill agency-report-pdf -a claude-code`. Or copy the skill folder (skills/agency-report-pdf in zubair-trabzada/ai-agency-claude) into .claude/skills/agency-report-pdf in your project. Claude Code loads it when a task matches its description.

How do I install Agency Report PDF in Codex?

Run `npx skills add zubair-trabzada/ai-agency-claude --skill agency-report-pdf -a codex`. Or copy the skill folder (skills/agency-report-pdf in zubair-trabzada/ai-agency-claude) into .agents/skills/agency-report-pdf in your project. Codex loads it when a task matches its description.

Can I use Agency Report PDF in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zubair-trabzada/ai-agency-claude --skill agency-report-pdf -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agency-report-pdf, .gemini/skills/agency-report-pdf, .github/skills/agency-report-pdf and .opencode/skills/agency-report-pdf in your project.

What does Agency Report PDF need to run?

Going by SKILL.md and its folder, Agency Report PDF needs the command-line tools its instructions call (python3 and pip3). Our summary lists: Python 3.

Does Agency Report PDF access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agency Report PDF safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agency Report PDF use?

Agency Report PDF is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agency Report PDF use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agency Report PDF?

Skills that share tags, products or a category with Agency Report PDF: Markitdown (ImCa0/just-laws, 781 stars), Gzh Design (isjiamu/gzh-design-skill, 4k stars), GenOffice Document CLI (genspark-ai/genoffice, 9.2k stars) and Harness Book Best Practice (wquguru/harness-books, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agency Report PDF?

zubair-trabzada (a GitHub user) maintains it in zubair-trabzada/ai-agency-claude, which has 151 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on April 8, 2026.

Source: zubair-trabzada/ai-agency-claude on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.