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.
curl -O https://raw.githubusercontent.com/Laksh-star/competitive-intelligence/main/CLAUDE.mdgit clone --depth 1 https://github.com/Laksh-star/competitive-intelligenceWrote 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/laksh-star/competitive-intelligence/claude-md)<a href="https://agentmods.dev/instructions/laksh-star/competitive-intelligence/claude-md"><img src="https://agentmods.dev/badge/instructions/laksh-star/competitive-intelligence/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/laksh-star/competitive-intelligence/claude-md"><img src="https://agentmods.dev/badge/instructions/laksh-star/competitive-intelligence/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 | $0.01239 | $0.01239 |
| Opus 5.5 | $0.00496 | $0.00496 |
| Sonnet 5.5 | $0.00248 | $0.00248 |
| Haiku 4.5 | $0.00124 | $0.00124 |
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]]: 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 postsintel_events- Extracted competitive intelligence events
Architecture
Data Flow
Sources → LLM Extraction → Dual Indexing → Query Handlers
-
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
-
LLM Extraction (main.py:136-164)
ExtractByLlmprocesses article content- Outputs structured
CompetitiveEventobjects with:event_type: product_launch, partnership, funding, key_hire, acquisitioncompetitor: Primary company involvedsignificance: high, medium, low (market impact)related_companies: Partners, investors, etc.
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.
- 2mo ago First seen · 132 lines · 1,239 tokens per session scan A 1b282129a391
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.
competitive-intelligence CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| competitive-intelligence CLAUDE.mdLaksh-star/competitive-intelligence | Instructions file | 23 | 4mo ago | MIT | A | 1,239 | Claude Code |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |