compete CLAUDE.md

compete CLAUDE.md is an instructions file for coding agents from lbj96347/compete. It costs 1,470 tokens per session, scanned A, original, MIT.

A Claude Code skill that turns a software repository into a competitive-intelligence report. Competitive intelligence means researching a product and its competitors to compare their capabilities and market position.

In plain words
What is it for?
Use it to extract product information, discover competitors, validate structured JSON results, and generate a self-contained HTML report.
Why use it?
It gives the research process a defined pipeline for analysing a repository, planning web research, normalising findings, and producing a browser-viewable report.

Instructions file

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.

agentmods
npx agentmods add instructions/lbj96347/compete/claude-md
Clone the repo
git clone --depth 1 https://github.com/lbj96347/compete

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 compete CLAUDE.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/lbj96347/compete/claude-md.svg)](https://agentmods.dev/instructions/lbj96347/compete/claude-md)
Your own site
<a href="https://agentmods.dev/instructions/lbj96347/compete/claude-md"><img src="https://agentmods.dev/badge/instructions/lbj96347/compete/claude-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,470 This file is loaded in full into every session.
When invoked 1,470 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
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 $0.01470 $0.01470
Opus 5 $0.00735 $0.00735
Sonnet 5 $0.00294 $0.00294
Haiku 4.5 $0.00147 $0.00147

Measured 3d ago against content hash 1b2d1812d553, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

compete 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 3d 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 · 106 lines

How it starts

The opening of the file, as written. The whole thing — 106 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.

What this is

compete is itself a Claude Code Skill (skills/compete/SKILL.md at the root), not a conventional app. It turns the repository Claude is invoked in into a competitive intelligence report. The deliverables are: Python helper scripts (stdlib only), JSON Schemas, reference docs, and a self-contained HTML report template. There is no package manager, build system, or test framework — just python and a browser.

When the skill runs, Claude itself is part of the pipeline: the scripts emit research plans, Claude executes them with WebSearch/WebFetch, and the scripts normalize Claude's findings back into schema-valid JSON.

Commands

Requirements: Python 3.9+ (stdlib only; jsonschema/referencing optional — scripts fall back to built-in structural checks when absent).

The pipeline runs in five stages. The two middle stages are two-phase (plan → Claude does web research → build):

# 1. Product Intelligence — repo → product.json
python skills/compete/scripts/analyze_repo.py --repo . --validate

# 2. Competitor Discovery (two-phase)
python skills/compete/scripts/discover_competitors.py plan  --product product.json        # → research plan (stdout)
#   ...Claude runs the plan with WebSearch/WebFetch → candidates.json...
python skills/compete/scripts/discover_competitors.py build --product product.json \
  --candidates candidates.json --validate                                  # → competitors.json

# 3. Intelligence Collection (two-phase)
python skills/compete/scripts/collect_intelligence.py plan  --competitors competitors.json
#   ...Claude runs the plan → findings.json...
python skills/compete/scripts/collect_intelligence.py build --competitors competitors.json \
  --findings findings.json --validate                                      # → companies/pricing/techstack/social/marketing/seo.json

# 4 + 5. Knowledge Graph + Report — all datasets → report.json + report.html
python skills/compete/scripts/build_report.py --input-dir . --output-dir ./insightkit-output --open

Read the full file on GitHub · 106 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. 3d ago First seen · 106 lines · 1,470 tokens per session scan A 1b2d1812d553

Subscribe to this mod's changes

compete CLAUDE.md is an instructions file published in the GitHub repository lbj96347/compete (9 stars, last pushed 2mo ago), licensed MIT. It adds 1,470 tokens to every session, about $0.0073 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.

Related

Other instructions, from other repositories

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,182 tokens

langchain AGENTS.md

AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.

langchain-ai/langchain · 4,345 tokens

vscode oss-third-party-notices.instructions.md

Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).

microsoft/vscode · 5,001 tokens

next.js AGENTS.md

Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,296 tokens