Pipe-level token filter that strips noisy CLI output before it reaches your LLM context window

Customer: Solo dev or indie hacker running LLM-powered coding agents (Claude Code, aider, cursor background agents) who shells out to git/npm/pytest and watches token costs spike from verbose stdout

Problem: Every npm install, git log, or pytest -v dumps 500-2000 tokens of low-signal output into agent context. At $15/M tokens, a busy dev session costs $5-20/day just on tool noise. No off-the-shelf pipe filter knows which parts matter to an LLM vs a human.

Pricing: freemium — $800 MRR in 4 months

Why now

Token-per-action cost is the #1 complaint in agent builder communities right now — the 91% reduction benchmarks circulating in the agent infra cluster have made this a named problem. Devs are actively searching for solutions, not waiting to be convinced.

Go-to-market

1. Post a before/after token count demo (git log raw vs filtered) to r/LocalLLaMA and Hacker News Show HN — attach a pip install one-liner, measure GitHub stars in 48h
2. Ship a Claude Code / aider-specific preset out of the box so the largest existing agent user base gets zero-config value — post in their Discord/GitHub discussions
3. Offer a free tier (OSS core, rule presets for git/npm/pytest/cargo) and charge $9/mo for a rules editor UI + team preset sync + usage dashboard — target the second dev on a team who can’t edit regex
4. DM 20 agent framework maintainers (aider, continue.dev, goose) and offer to co-author a blog post on token hygiene — get listed in their docs as a recommended pipe

Moat (or lack thereof)

No real moat. Regex pipes are trivially copyable and any LLM provider could bake this into their SDK. Defensibility comes only from preset quality (network effect of community-contributed rules) and being the first name devs type — that window is ~6 months before a funded competitor or OSS clone captures it.