2 paid slots open this week · for teams using Claude Code, Codex, Cursor, or agentic coding

Find where your AI agents are burning money.

A 48-hour repo audit that identifies avoidable context churn, repeated exploration, oversized prompts, fragile tool loops, and wasted agent runs. You get a report, a GitHub Action leak gate, and a fix path.

Payment through a service-appropriate invoice reserves the slot. After payment, send repo/access details by email or private intake; do not paste private code into a public issue.

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What gets audited

The audit is aimed at teams already paying for AI coding tools and losing time or budget to avoidable agent behavior.

Context Waste

Repeated grep/read loops, missing repo maps, oversized prompts, and files that agents keep rediscovering.

Loop Risk

Commands, tests, and plans that cause agents to circle without producing a verified patch or answer.

Fix Surface

Hook, prompt, repo-doc, CI gate, or workflow changes that reduce the next run, not just describe the current waste.

48-hour deliverables

Day 1Repo scan, current AI workflow intake, agent-run risk map, and first pass cost-leak report.
Day 2Measurement harness where feasible, GitHub Action threshold gate, context or workflow patch, final report, and operator runbook.
Done meansYou can point the next agent run at better context and see exactly what changed.
v2.9.0 Action release Green Action smoke test Action metadata

Pricing

Two clean ways to buy. The $1,000 audit is the goal because it includes implementation, not just diagnosis.

Diagnostic

$500
  • One repo or workflow reviewed.
  • Cost leak report and top fixes.
  • Async handoff note.
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AI Agent Cost Leak Audit

$1,000
  • Audit plus one concrete fix path.
  • Repo map, hook, prompt, or GitHub Action leak gate where appropriate.
  • Report and runbook.
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Handoff Plus

$1,500
  • Everything in the sprint.
  • Team-facing rollout notes.
  • One revision pass after the first production run.
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Send one repo. Get the leaks back.

Best inputs: GitHub repo, recent agent transcript or rough workflow, monthly AI-coding spend if known, and the next task your team wants the agent to handle faster.

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