agency-pipeline

agency-pipeline is a skill for Claude Code, Codex from zubair-trabzada/ai-agency-claude. It costs 24 tokens per session (3,478 once invoked), scanned A, original, MIT.

A prospect-pipeline workflow that scans audit and analysis files in the current directory and turns them into a staged, scored view of potential clients. A sales pipeline is a list of prospects organized by their progress toward becoming customers.

In plain words
What is it for?
Use it to build a pipeline report from agency audit files, classify prospects by stage, calculate revenue projections, and sort the resulting opportunities.
Why use it?
It brings scattered audit results into one overview. It helps show each prospect’s stage, composite score, completed analysis, and possible revenue.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to build a pipeline report from agency audit files, classify prospects by stage, calculate revenue projections, and sort the resulting opportunities.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zubair-trabzada/ai-agency-claude/agency-pipeline
Install

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.

Any agent
npx skills add zubair-trabzada/ai-agency-claude --skill agency-pipeline
Clone the repo
git clone --depth 1 https://github.com/zubair-trabzada/ai-agency-claude

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for agency-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/skills/zubair-trabzada/ai-agency-claude/agency-pipeline/github.svg)](https://agentmods.dev/skills/zubair-trabzada/ai-agency-claude/agency-pipeline)
Your own site
<a href="https://agentmods.dev/skills/zubair-trabzada/ai-agency-claude/agency-pipeline"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-agency-claude/agency-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.

agentmods 80×15 button for agency-pipeline

Your own site · 80×15
<a href="https://agentmods.dev/skills/zubair-trabzada/ai-agency-claude/agency-pipeline"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-agency-claude/agency-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,478 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00024 $0.03478
Opus 5 $0.00012 $0.01739
Sonnet 5 $0.00005 $0.00696
Haiku 4.5 $0.00002 $0.00348

Measured 12d ago against content hash e4cc75c4df18, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

agency-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 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.

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.

skills/agency-pipeline/SKILL.md · 390 lines

How it starts

The opening of the file, as written. The whole thing — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

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:

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)

Read the full file on GitHub · 390 lines

Changes

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.

  1. 12d ago First seen · 390 lines · 24 tokens per session scan A e4cc75c4df18

Subscribe to this mod's changes

agency-pipeline is a skill published in the GitHub repository zubair-trabzada/ai-agency-claude (137 stars, last pushed 5mo ago), licensed MIT. It adds 24 tokens to every session and 3,478 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 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens