Borrowing it
Nothing to install: this file belongs to Dangooy/trade-pipeline-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Dangooy/trade-pipeline-skill/main/CLAUDE.mdgit clone --depth 1 https://github.com/Dangooy/trade-pipeline-skillWrote 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/instructions/dangooy/trade-pipeline-skill/claude-md)<a href="https://agentmods.dev/instructions/dangooy/trade-pipeline-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dangooy/trade-pipeline-skill/claude-md/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/instructions/dangooy/trade-pipeline-skill/claude-md"><img src="https://agentmods.dev/badge/instructions/dangooy/trade-pipeline-skill/claude-md.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 | $0.00942 | $0.00942 |
| Opus 5 | $0.00471 | $0.00471 |
| Sonnet 5 | $0.00188 | $0.00188 |
| Haiku 4.5 | $0.00094 | $0.00094 |
Grade A, and why
trade-pipeline-skill 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — Instructions for AI Agents
Quick Start
# Create and activate a virtual environment first.
# macOS / Linux:
python3 -m venv .venv && source .venv/bin/activate
# Windows (PowerShell):
# py -m venv .venv; .\.venv\Scripts\Activate.ps1
pip install -e .
python -m trade_pipeline --input examples/sample_inquiry.xlsx --order TEST01 --buyer global_fasteners
Always work inside the venv. Once activated, python and pip resolve to the venv's
interpreter on every platform — no python vs python3 branching needed. Installing into the
system interpreter is not an option on many setups: Homebrew and Debian mark theirs
externally-managed (PEP 668) and will refuse pip install outright.
Output goes to output/TEST01/. Steps 1–7 generate: rfq.json, model.json, quotation.xlsx, pi.xlsx, ci.xlsx. Step 8 (PL) is optional and requires the separate pl-gen package.
Project Structure
trade_pipeline/
├── extractors/ # Step 1: Excel → ExtractedDocument
├── understanding/ # Steps 2-4: parse → canonicalize → assemble OrderModel
├── models/ # OrderModel dataclass (single source of truth)
├── writers/ # Steps 5-8: generate Excel documents from OrderModel
├── validation/ # review.json mechanism for human-in-the-loop
├── adapters/ # PL config resolution
├── legacy/ # Bridge to external pl-gen (optional)
├── pipeline/ # Main orchestrator (main.py) + price updater
└── config/ # config.yaml (sellers, buyers, terms, formats)
Privacy: --use-llm
The optional --use-llm flag sends the raw inquiry content (customer name, products, quantities)
to the Anthropic API for parsing. It is off by default and opt-in. The default rule-based
parser runs fully offline and sends nothing over the network. Avoid --use-llm for sensitive or
confidential documents.
Key Rules
- All Writers read from OrderModel only — never re-parse Excel in a Writer
- Buyer match failure = hard block — generates review.json, pipeline stops
- UUID anchoring — quotation has hidden UUID column; price write-back uses UUID, not row numbers
- Config-driven — change
config/config.yamlto add sellers, buyers, terms, formats
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.
- 8d ago First seen · 83 lines · 942 tokens per session scan A 521a128ae981
trade-pipeline-skill CLAUDE.md is an instructions file published in the GitHub repository Dangooy/trade-pipeline-skill (24 stars, last pushed 4d ago), licensed MIT. It adds 942 tokens to every session, about $0.0047 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.
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