New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Agency Pipeline

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

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

MITAuto-check passedSales & Support

Install Agency Pipeline

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

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

GitHub CLI
$ gh skill install zubair-trabzada/ai-agency-claude agency-pipeline --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-pipeline .claude/skills/agency-pipeline && 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-pipeline
GitHub stars
151
Token cost
~3.3k tokens
SKILL.md length
992 words
Files
1
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 10 steps: Discover All Audit Files → Parse Each File and Extract Prospect Data → Consolidate by Prospect → …
  • Sales & Support work in your project
  • SKILL.md covers Invocation, Execution Flow, Edge Cases and Important Rules
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agency Pipeline is an agent skill from zubair-trabzada/ai-agency-claude. Prospect Pipeline Manager — scans all audit files to build a scored, staged pipeline view with revenue projections

Its SKILL.md is about 3.3k 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 Sales & Support. 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

  • Sales & Support work in your project

Example prompts

  • “/agency-pipeline”

Workflow steps

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

  1. Discover All Audit Files
  2. Parse Each File and Extract Prospect Data
  3. Consolidate by Prospect
  4. Calculate Composite Scores
  5. Classify Pipeline Stage
  6. Calculate Revenue Potential
  7. Calculate Opportunity Score
  8. Build Pipeline Summary Statistics
  9. Generate the Pipeline Report
  10. Display Terminal Summary

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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and markdown).

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

  • Network

    No URLs in SKILL.md.

    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 Pipeline loads about 3.3k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 992 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

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). 992 words, ~3,310 tokens.

Download SKILL.mdSave it as .claude/skills/agency-pipeline/SKILL.md (or your agent's skills folder).
name
agency-pipeline
description
Prospect Pipeline Manager — scans all audit files to build a scored, staged pipeline view with revenue projections

Prospect Pipeline Manager

You are the pipeline management engine for the AI Agency Command Center. When the user runs /agency pipeline, you scan the current directory for ALL audit and analysis files across every tool suite, build a comprehensive pipeline view with composite scores, classify each prospect by stage, calculate revenue potential, and output a sortable pipeline report.

This gives the agency operator a bird's-eye view of every prospect they've ever analyzed — where they stand, what's been done, and where the money is.

Invocation

/agency pipeline

No arguments required. Operates on the current working directory.

Execution Flow

Step 1 — Discover All Audit Files

Use Bash to find every audit/analysis file in the current working directory:

bash
ls -la *.md 2>/dev/null | grep -iE "(AGENCY-ONBOARD|AGENCY-PROPOSAL|MARKETING-AUDIT|PROSPECT-ANALYSIS|REPUTATION-AUDIT|REPUTATION-SCORECARD|GEO-AUDIT|GEO-REPORT|LEGAL-COMPLIANCE|SALES-PROPOSAL|COMPETITIVE-INTEL|BRAND-MENTIONS|AGENCY-PIPELINE|AGENCY-REPORT)" 2>/dev/null

Also run a broader scan to catch files with non-standard naming:

bash
ls -la *.md 2>/dev/null

Review all .md files for audit-related content by checking the first 10 lines of each file for score indicators, audit headers, or company analysis markers.

Step 2 — Parse Each File and Extract Prospect Data

For EACH audit file found, read it and extract:

Prospect Identification:

  • Company name (from the file title or document header)
  • URL (if present in the document)
  • Industry/business type
  • Location

Scores (if present):

  • Marketing score (0-100)
  • Reputation score (0-100)
  • GEO/SEO score (0-100)
  • Legal score (0-100)
  • Sales opportunity score (0-100)
  • Composite/agency score (if pre-calculated)

File metadata:

  • File name
  • Which tool suite generated it (Marketing, Reputation, GEO, Legal, Sales, Agency)
  • Date (from file content or filesystem)

Key data points:

  • Number of critical findings
  • Top critical finding (single most impactful issue)
  • Recommended tier (if a proposal exists)
  • Proposed pricing (if a proposal exists)
Step 3 — Consolidate by Prospect

Group all files by company/prospect name. A single prospect may have multiple files across different tool suites.

For each unique prospect, build a consolidated record:

PROSPECT: [Company Name]
URL: [URL if known]
Industry: [Industry]
Location: [Location]
Files: [List of all files for this prospect]
Suites Completed: [Which of the 5 tool suites have been run]
Suites Pending: [Which haven't been run yet]

Scores:
- Marketing: [score or "—"]
- Reputation: [score or "—"]
- GEO/SEO: [score or "—"]
- Legal: [score or "—"]
- Sales: [score or "—"]
- Composite: [calculated or "—"]
Step 4 — Calculate Composite Scores

For prospects with scores from multiple tool suites, calculate the composite:

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

If not all scores are available, use adjusted weights:

Only recalculate weights proportionally across available dimensions. For example, if only Marketing (25%), Reputation (20%), and GEO (20%) are available:

  • Total available weight: 65%
  • Adjusted: Marketing = 25/65 = 38.5%, Reputation = 20/65 = 30.8%, GEO = 20/65 = 30.8%

Apply the adjusted weights and note the score is partial:

Composite (Partial — 3/5 dimensions) = [score]
Step 5 — Classify Pipeline Stage

Assign each prospect to a pipeline stage based on what work has been completed:

StageCriteriaIcon
New LeadOnly a quick scan or single audit exists. No comprehensive analysis.[1]
AuditedFull onboard report exists, OR 3+ individual audit files exist. Comprehensive data available.[2]
ProposedAn AGENCY-PROPOSAL-*.md file exists for this prospect. Pricing has been presented.[3]
Active ClientThe user manually marks a prospect as active, OR a proposal file contains acceptance indicators.[4]

Stage detection logic:

IF AGENCY-PROPOSAL-*.md exists for this prospect:
    stage = "Proposed" [3]
ELSE IF AGENCY-ONBOARD-*.md exists OR (3+ audit files from different suites exist):
    stage = "Audited" [2]
ELSE:
    stage = "New Lead" [1]

Note: "Active Client" stage requires manual tagging by the user. If any file contains text indicating active engagement (e.g., "ACTIVE CLIENT" or "ENGAGED"), classify accordingly.

Step 6 — Calculate Revenue Potential

Estimate the revenue potential for each prospect based on available data:

Method 1: From Proposal Data (if a proposal exists)

  • Use the recommended tier pricing directly
  • Essentials: midpoint of the Essentials range
  • Growth: midpoint of the Growth range
  • Full Agency: midpoint of the Full Agency range

Method 2: From Scores (if no proposal but scores exist) Use the composite score to estimate which tier they'd likely need:

Composite ScoreLikely TierEstimated Monthly Revenue
0-30Full Agency$5,500/month
31-45Full Agency or Growth$4,000/month
46-60Growth$2,500/month
61-75Growth or Essentials$1,500/month
76-100Essentials$800/month

Lower scores = more problems = higher revenue potential from agency services.

Method 3: From Industry (if minimal data) Use industry average estimates:

IndustryEstimated Monthly Potential
Local Service (HVAC, Plumbing, Roofing)$2,000-$3,500/month
Professional Services (Legal, Accounting)$2,500-$4,000/month
Healthcare/Medical$3,000-$5,000/month
SaaS/Software$3,500-$6,000/month
E-commerce$2,500-$5,000/month
Restaurant/Hospitality$1,500-$2,500/month
Real Estate$2,000-$3,500/month
Show full SKILL.md (379 more words)Show less
Step 7 — Calculate Opportunity Score

Create an Opportunity Score (0-100) that combines multiple factors to rank prospects by pursuit priority:

Opportunity Score = (Need Score x 0.40) + (Fit Score x 0.30) + (Readiness Score x 0.30)

Need Score (based on composite audit score — inverted):

  • Composite 0-30 = Need 90-100 (desperate need)
  • Composite 31-50 = Need 70-89 (significant need)
  • Composite 51-70 = Need 50-69 (moderate need)
  • Composite 71-100 = Need 20-49 (low need)
  • Formula: Need = 100 - Composite (capped at 100)

Fit Score (based on sales opportunity score or industry fit):

  • If Sales score exists, use it directly
  • Otherwise, assign based on industry (local service businesses = 80, SaaS = 70, etc.)

Readiness Score (based on pipeline stage and engagement):

  • Proposed = 90 (they've seen pricing, awaiting decision)
  • Audited = 60 (full data, ready for proposal)
  • New Lead = 30 (needs more analysis)
Step 8 — Build Pipeline Summary Statistics

Calculate aggregate pipeline metrics:

PIPELINE SUMMARY:
- Total Prospects: [count]
- By Stage: [X] New Leads | [X] Audited | [X] Proposed | [X] Active
- Total Pipeline Value: $[sum of all monthly revenue potentials]/month
- Average Composite Score: [average across all scored prospects]
- Highest Opportunity: [prospect name] (Opportunity Score: [X])
- Suites Used: Marketing [X] | Reputation [X] | GEO [X] | Legal [X] | Sales [X]
Step 9 — Generate the Pipeline Report

Write the report to AGENCY-PIPELINE.md:

markdown
# Agency Pipeline Report

**Generated:** [Current Date]
**Working Directory:** [current directory path]

---

## Pipeline Summary

| Metric | Value |
|--------|-------|
| Total Prospects | [X] |
| New Leads | [X] |
| Audited | [X] |
| Proposed | [X] |
| Active Clients | [X] |
| Total Pipeline Value | $[X]/month |
| Average Composite Score | [X]/100 |

---

## Pipeline Overview

*Sorted by Opportunity Score (highest first)*

| # | Company | Stage | Composite | Opportunity | Monthly Value | Top Issue | Suites |
|---|---------|-------|-----------|-------------|---------------|-----------|--------|
| 1 | [Name] | [Stage] | [XX/100] | [XX/100] | $[X]/mo | [One-line issue] | [M/R/G/L/S] |
| 2 | [Name] | [Stage] | [XX/100] | [XX/100] | $[X]/mo | [One-line issue] | [M/R/G/L/S] |
[... all prospects]

*Suites Key: M=Marketing, R=Reputation, G=GEO/SEO, L=Legal, S=Sales*

---

## Stage Breakdown

### [3] Proposed — Awaiting Decision

[For each proposed prospect:]

**[Company Name]** — Composite: [XX]/100 | Proposed Value: $[X]/month
- Proposed Tier: [Tier name]
- Proposal Date: [Date if available]
- Key selling point: [The most compelling reason for them to sign]
- Follow-up action: [What to do next]

---

### [2] Audited — Ready for Proposal

[For each audited prospect:]

**[Company Name]** — Composite: [XX]/100 | Estimated Value: $[X]/month
- Audits completed: [List of suites]
- Top critical finding: [Single most impactful issue]
- Recommended action: Run `/agency propose "[Company Name]"` to generate proposal

---

### [1] New Leads — Needs Analysis

[For each new lead:]

**[Company Name]** — Available Score: [XX]/100 | Estimated Value: $[X]/month
- Data available: [What files exist]
- Recommended action: Run `/agency onboard <url>` for full analysis

---

## Revenue Projections

| Tier | Prospects | Monthly Revenue | Annual Revenue |
|------|-----------|----------------|---------------|
| Essentials ($500-$1,500) | [X] | $[total] | $[total x 12] |
| Growth ($1,500-$3,500) | [X] | $[total] | $[total x 12] |
| Full Agency ($3,500-$7,500) | [X] | $[total] | $[total x 12] |
| **Total Pipeline** | **[X]** | **$[total]** | **$[total x 12]** |

---

## Dimension Health Across Pipeline

*Average scores across all audited prospects*

| Dimension | Avg Score | Lowest Prospect | Highest Prospect |
|-----------|-----------|-----------------|-----------------|
| Marketing | [XX] | [Name] ([XX]) | [Name] ([XX]) |
| Reputation | [XX] | [Name] ([XX]) | [Name] ([XX]) |
| GEO/SEO | [XX] | [Name] ([XX]) | [Name] ([XX]) |
| Legal | [XX] | [Name] ([XX]) | [Name] ([XX]) |
| Sales Opp. | [XX] | [Name] ([XX]) | [Name] ([XX]) |

---

## Recommended Next Actions

1. **[Highest priority action]** — [Why and what to do]
2. **[Second priority action]** — [Why and what to do]
3. **[Third priority action]** — [Why and what to do]

---

## File Index

*All audit files found in this directory*

| File | Prospect | Suite | Date |
|------|----------|-------|------|
| [filename.md] | [Company] | [Suite] | [Date] |
[... all files]

---

*Generated by the AI Agency Command Center*
*Run `/agency propose "[Company Name]"` to generate a proposal for any prospect*
*Run `/agency onboard <url>` to add a new prospect to the pipeline*
Step 10 — Display Terminal Summary

After saving the file, display a compact terminal summary:

AGENCY PIPELINE — [X] Prospects
═══════════════════════════════════════════
 [3] Proposed:  [X] prospects — $[X]/month potential
 [2] Audited:   [X] prospects — $[X]/month potential
 [1] New Leads: [X] prospects — $[X]/month potential
═══════════════════════════════════════════
 TOTAL PIPELINE: $[X]/month ($[X x 12]/year)

 TOP OPPORTUNITY: [Company Name] — Score: [XX] — $[X]/month
 NEXT ACTION: [What to do with the top opportunity]

 Pipeline saved to AGENCY-PIPELINE.md

Edge Cases

No Files Found

If no audit files exist in the current directory:

No prospect data found in the current directory.

Get started:
  /agency onboard <url>  — Run a full audit on a business
  /agency quick <url>    — Quick 60-second snapshot

Your pipeline will build as you audit more prospects.
Single Prospect Only

If only one prospect is found, still generate the full pipeline report. Note: "Pipeline currently contains 1 prospect. Continue auditing businesses to build your pipeline."

Duplicate Company Names

If files appear to reference the same company with slightly different names (e.g., "Acme Plumbing" vs "Acme Plumbing LLC"), consolidate them under the more complete name and list all associated files.

Stale Data

If file modification dates are more than 90 days old, flag the prospect as "Stale — consider re-auditing" in the pipeline view.

Important Rules

  1. Read every file. Don't guess scores from filenames. Actually read each file and extract the real scores.
  2. Deduplicate prospects. Multiple files about the same company should be consolidated into one pipeline entry.
  3. Sort by opportunity. The pipeline should always be sorted by Opportunity Score (highest first) so the best prospects are at the top.
  4. Conservative revenue estimates. When in doubt, use the lower end of revenue ranges.
  5. Always save to file. Unlike /agency quick, the pipeline report is always saved to AGENCY-PIPELINE.md.
  6. Overwrite previous pipeline. If AGENCY-PIPELINE.md already exists, overwrite it with the latest data. The pipeline should always reflect current state.
  7. Include all prospects. Never filter out prospects. Even stale or low-opportunity leads should appear (they can be re-evaluated).

© 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-pipeline of zubair-trabzada/ai-agency-claude.

Open the folder on GitHubat commit 172a6c2

Compare with similar skills

Agency Pipeline 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 Pipeline compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agency Pipeline this skillzubair-trabzada/ai-agency-claude151—~3.3kAutomated safety check: PassMIT
Cold Outbound Optimizerericosiu/ai-marketing-skills3.6k1 repos~1.7kAutomated safety check: PassMIT
Doc Coauthoringaws-samples/sample-strands-agent-with-agentcore19540 repos~3.2kAutomated safety check: PassMIT
Amazon Buy Box Monitorbrowser-act/skills6.1k1 repos~1.6kAutomated safety check: PassMIT
Review Analysisliangdabiao/amazon-sorftime-research-MCP-skill9591 repos~2.5kAutomated safety check: PassNone
Deskcomm Extensaomelgarafael/DeskcommCRM4.5k—~2.7kAutomated safety check: PassMIT

Similar skills

  • Cold Outbound Optimizer

    ericosiu/ai-marketing-skills

    Design, analyze, and optimize cold outbound email campaigns for Instantly.

    3.6k GitHub starsUsed in 1 repo~1.7k tokens
    Sales & SupportAuto-check passed

  • Doc Coauthoring

    aws-samples/sample-strands-agent-with-agentcore

    Official

    Guide users through a structured workflow for co-authoring documentation.

    195 GitHub starsUsed in 40 repos~3.2k tokens
    Sales & SupportAuto-check passed

  • Amazon Buy Box Monitor

    browser-act/skills

    Pulls Amazon product details, competing seller prices and seller ratings for a given ASIN through the BrowserAct API, without browser automation.

    6.1k GitHub starsUsed in 1 repo~1.6k tokens
    Sales & SupportAuto-check passed

  • Review Analysis

    liangdabiao/amazon-sorftime-research-MCP-skill

    对亚马逊商品评论进行深度分析，自动识别产品痛点、分析退货原因，生成改进建议和客服回复模板。Invoke when user uses /review-analysis command with a product ASIN.

    959 GitHub starsUsed in 1 repo~2.5k tokens
    Sales & SupportAuto-check passed

  • Deskcomm Extensao

    melgarafael/DeskcommCRM

    Guia para criar uma extensão do DeskcommCRM — o pacote declarativo — em vez de abrir um PR no núcleo.

    4.5k GitHub stars~2.7k tokensUpdated today
    Sales & SupportAuto-check passed

  • Tourmind Booking

    tourmind-com/Tourmind-Booking-Skills

    MUST USE for any hotel or accommodation intent in any language, including hotel search, hotel recommendations, nearby accommodation, hostels, guesthouses, resorts, where-to-stay questions, room…

    1.8k GitHub stars~13k tokensUpdated yesterday
    Sales & SupportAuto-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 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 Report PDF

    zubair-trabzada/ai-agency-claude

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

    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

Categories

Questions about Agency Pipeline

What does Agency Pipeline do?

Prospect Pipeline Manager — scans all audit files to build a scored, staged pipeline view with revenue projections. Agency Pipeline is an agent skill from zubair-trabzada/ai-agency-claude.

When should I use Agency Pipeline?

Agency Pipeline fits situations like: sales & Support work in your project.

How do I install Agency Pipeline in Claude Code?

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

How do I install Agency Pipeline in Codex?

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

Can I use Agency Pipeline 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-pipeline -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-pipeline, .gemini/skills/agency-pipeline, .github/skills/agency-pipeline and .opencode/skills/agency-pipeline in your project.

What does Agency Pipeline need to run?

SKILL.md names no scripts, command-line tools or credentials: Agency Pipeline is instructions for the agent only.

Does Agency Pipeline 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 Pipeline 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 Pipeline use?

Agency Pipeline 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 Pipeline use?

About 3.3k tokens (SKILL.md is roughly 13k 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 Pipeline?

Skills that share tags, products or a category with Agency Pipeline: Cold Outbound Optimizer (ericosiu/ai-marketing-skills, 3.6k stars), Doc Coauthoring (aws-samples/sample-strands-agent-with-agentcore, 195 stars), Amazon Buy Box Monitor (browser-act/skills, 6.1k stars) and Review Analysis (liangdabiao/amazon-sorftime-research-MCP-skill, 959 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agency Pipeline?

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.