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mcp-content-pipeline: Instructions file for Claude Code

CLAUDE.md

mcp-content-pipeline CLAUDE.md is an instructions file for Claude Code from berkayildi/mcp-content-pipeline. It costs 737 tokens per session, scanned A, original, MIT.

Project instructions for an MCP server that analyses YouTube videos and content, with optional connections to X and GitHub. MCP is a way for AI tools to call outside services.

In plain words
What is it for?
Use it to install dependencies, run tests, start the server, configure service access, and understand where tools, API clients, and data models live.
Why use it?
It gives a coding agent the project’s setup, structure, required keys, and testing commands, so work follows the repository’s documented rules.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: positional $N argument.

This is berkayildi/mcp-content-pipeline's own configuration. It tells Claude Code how to work on mcp-content-pipeline itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything mcp-content-pipeline configures →

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is make benchmark-copy # Copy results to ../llm-benchmarks/text-generation/.

not rated 0repo 4mo ago A scan 737 tokens original MIT
View source ↗ berkayildi/mcp-content-pipeline pypi.org
Reuse

Borrowing it

Nothing to install: this file belongs to berkayildi/mcp-content-pipeline. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/berkayildi/mcp-content-pipeline/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/berkayildi/mcp-content-pipeline

Made for: Claude Code.

Wrote this? Show the measurements

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<a href="https://agentmods.dev/instructions/berkayildi/mcp-content-pipeline/claude-md"><img src="https://agentmods.dev/badge/instructions/berkayildi/mcp-content-pipeline/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 737 This file is loaded in full into every session.
When invoked 737 The same file — it is already loaded in full.
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 $0.00737 $0.00737
Opus 5 $0.00368 $0.00368
Sonnet 5 $0.00147 $0.00147
Haiku 4.5 $0.00074 $0.00074

Measured 11d ago against content hash 27f9a1032078, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

mcp-content-pipeline CLAUDE.md 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 11d 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.

CLAUDE.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

mcp-content-pipeline

YouTube video analysis and content pipeline exposed as MCP tools.

Quick Start

uv sync
uv run pytest
uv run mcp-content-pipeline

Architecture

  • src/mcp_content_pipeline/server.py — MCP server entry point, registers all tools
  • src/mcp_content_pipeline/tools/ — one file per MCP tool
  • src/mcp_content_pipeline/services/ — API clients (YouTube, Claude, GitHub)
  • src/mcp_content_pipeline/models/ — Pydantic schemas

Environment Variables

All prefixed with MCP_CP_:

  • MCP_CP_ANTHROPIC_API_KEY — required
  • MCP_CP_YOUTUBE_API_KEY — optional (only for list_channel_videos)
  • MCP_CP_SUPADATA_API_KEY — required for YouTube transcript extraction
  • MCP_CP_GITHUB_TOKEN — required for sync_to_github
  • MCP_CP_GITHUB_REPO — format: "owner/repo"
  • MCP_CP_GITHUB_BRANCH — branch to push to (default: main)
  • MCP_CP_GITHUB_OUTPUT_DIR — output directory for YouTube analyses (default: content/youtube)
  • MCP_CP_CLAUDE_MODEL — default: claude-sonnet-4-6
  • MCP_CP_MAX_TRANSCRIPT_TOKENS — max transcript length in tokens (default: 100000)
  • MCP_CP_X_BEARER_TOKEN — required for analyse_x_feed
  • MCP_CP_X_ACCOUNTS — comma-separated X usernames
  • MCP_CP_X_TOPICS — comma-separated topics (default: AI,tech)
  • MCP_CP_GEMINI_API_KEY — required for generate_image
  • MCP_CP_GEMINI_MODEL — default: gemini-3.1-flash-image-preview
  • MCP_CP_IMAGE_OUTPUT_DIR — directory for generated images (default: ~/Downloads)

Testing

uv run pytest -v --cov=src/mcp_content_pipeline
uv run ruff check src/ tests/

Eval Gate

# Run eval locally
pip install mcp-llm-eval anthropic openai google-genai
mcp-llm-eval run --config .eval-gate.yml --dataset eval/dataset.json --output-dir eval/results
mcp-llm-eval check --results eval/results/latest_summary.json --config .eval-gate.yml

Triggered automatically on PRs that change prompt files or model config. Benchmarks Claude Sonnet vs Gemini 2.5 Flash.

Benchmark

make benchmark        # Run eval against all 8 models (~$0.68, ~5 minutes)
make benchmark-copy   # Copy results to ../llm-benchmarks/text-generation/

Read the full file on GitHub · 74 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. 11d ago First seen · 74 lines · 737 tokens per session scan A 27f9a1032078

Subscribe to this mod's changes

mcp-content-pipeline CLAUDE.md is an instructions file published in the GitHub repository berkayildi/mcp-content-pipeline (0 stars, last pushed 4mo ago), licensed MIT. It adds 737 tokens to every session, about $0.0037 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 2026-08-31.

Building agentsOpenAI APIResearchanthropic-apigoogle-apimcpx-apiyoutube-api
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