competitive-intelligence: Instructions file for Claude Code

CLAUDE.md

competitive-intelligence CLAUDE.md is an instructions file for Claude Code from Laksh-star/competitive-intelligence. It costs 1,239 tokens per session, scanned A, original, MIT.

Project instructions for a pipeline that monitors competitor mentions across the web and extracts events such as launches, partnerships, funding, and hires. It uses PostgreSQL, a database system, to store articles and extracted events.

In plain words
What is it for?
Use them when setting up the project, editing environment variables, running a one-time or continuous sync, testing results, or inspecting its PostgreSQL data.
Why use it?
They explain how to install, configure, run, and test the monitoring pipeline. They also identify the main data flow and the database tables used by the project.

Instructions file for Claude Code

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

This is Laksh-star/competitive-intelligence's own configuration. It tells Claude Code how to work on competitive-intelligence 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 competitive-intelligence configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Laksh-star/competitive-intelligence. 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/Laksh-star/competitive-intelligence/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/Laksh-star/competitive-intelligence

Made for: Claude Code.

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Per session 1,239 This file is loaded in full into every session.
When invoked 1,239 The same file — it is already loaded in full.
Security scan A 1 finding. 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 $0.01239 $0.01239
Opus 5.5 $0.00496 $0.00496
Sonnet 5.5 $0.00248 $0.00248
Haiku 4.5 $0.00124 $0.00124

Measured 2mo ago against content hash 1b282129a391, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

competitive-intelligence CLAUDE.md scanned grade A with 1 finding 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 2mo 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

async def fetch(self) -> list[dict[str, Any]]:
CLAUDE.md · 132 lines

How it starts

The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

Competitive Intelligence Monitor - A CocoIndex pipeline that tracks competitor mentions across the web using Tavily AI Search. Uses DeepSeek LLM-powered extraction to identify product launches, partnerships, funding rounds, key hires, and strategic moves.

Development Commands

Setup

pip install -e .                    # Install dependencies
cp .env.example .env                # Create environment config
# Edit .env with your API keys and database URL

Running the Pipeline

cocoindex update main -f            # One-time sync (force refresh)
cocoindex update -L main.py         # Live mode (continuous monitoring)

Testing

python3 test_results.py             # Run comprehensive tests

See TESTING.md for detailed testing procedures.

Database Setup

Requires PostgreSQL. The pipeline auto-creates tables:

  • intel_articles - Raw news articles and blog posts
  • intel_events - Extracted competitive intelligence events

Architecture

Data Flow

Sources → LLM Extraction → Dual Indexing → Query Handlers
  1. Sources (main.py:49-99)

    • TavilySearchSource - AI-native web search optimized for agents
    • Searches with query: "{competitor} AND (funding OR partnership OR product launch OR acquisition OR executive hire)"
    • Returns clean, full article content (not just snippets)
    • Configurable via .env: COMPETITORS, REFRESH_INTERVAL_SECONDS, SEARCH_DAYS_BACK
  2. LLM Extraction (main.py:136-164)

    • ExtractByLlm processes article content
    • Outputs structured CompetitiveEvent objects with:
      • event_type: product_launch, partnership, funding, key_hire, acquisition
      • competitor: Primary company involved
      • significance: high, medium, low (market impact)
      • related_companies: Partners, investors, etc.

Read the full file on GitHub · 132 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. 2mo ago First seen · 132 lines · 1,239 tokens per session scan A 1b282129a391

Subscribe to this mod's changes

competitive-intelligence CLAUDE.md is an instructions file published in the GitHub repository Laksh-star/competitive-intelligence (23 stars, last pushed 4mo ago), licensed MIT. It adds 1,239 tokens to every session, about $0.0050 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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