claude
0543a43816
CC0-licensed methodology for estimating the environmental and social costs of AI conversations (20+ categories), plus a reusable toolkit for automated impact tracking in Claude Code sessions.
1.8 KiB
Plan: Define when to use (and not use) this tool
Target sub-goals: 1 (estimate before acting), 3 (value per token), 12 (honest arithmetic)
Problem
Not every task justifies the cost of an LLM conversation. A grep command costs ~0 Wh. A Claude Code session costs ~6-250 Wh. Many tasks that people bring to AI assistants could be done with simpler tools at a fraction of the cost. Without explicit guidelines, the default is to use the most powerful tool available, not the most appropriate one.
Actions
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Create a decision framework. A simple flowchart or checklist:
- Can this be done with a shell command, a search engine query, or reading documentation? If yes, do that instead.
- Does this task require generating or transforming text/code that a human would take significantly longer to produce? If yes, an LLM may be justified.
- Will the output reach many people or prevent significant harm? If yes, the cost is more likely justified.
- Is this exploratory/speculative, or targeted with clear success criteria? Prefer targeted tasks.
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Integrate into CLAUDE.md. Add the framework as a quick-reference so it's loaded into every conversation.
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Track adherence. When a conversation ends, note whether the task could have been done with a simpler tool. Feed this back into the impact log.
Success criteria
- The user (and I) have a shared understanding of when the cost is justified.
- Measurable reduction in conversations spent on tasks that don't need an LLM.
Honest assessment
High value but requires discipline from both sides. The framework itself is cheap to create. The hard part is actually following it — especially when the LLM is convenient even for tasks that don't need it. This plan is more about establishing a norm than building a tool.