impact

impact is a skill for Claude Code, Codex from huiruru/ecoguilt. It costs 28 tokens per session (1,849 once invoked), scanned A, original, MIT.

A report of the environmental cost of the current AI coding session, including carbon dioxide emissions, water use, energy, tokens, model, and cost.

In plain words
What is it for?
Use it to inspect the session's recorded impact numbers and compare them with the stated smarter-model alternative.
Why use it?
It gives you the recorded environmental and usage figures for a session and can show what a different model choice might have saved.

Skill for Claude CodeCodex

Part of the ecoguilt plugin — 3 skills, 2 hooks shipped together

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 skills/huiruru/ecoguilt/impact
Any agent
npx skills add huiruru/ecoguilt --skill impact
Clone the repo
git clone --depth 1 https://github.com/huiruru/ecoguilt

Made for: Claude Code, Codex.

Or install ecoguilt, the plugin that ships this one along with the rest of its 3 skills, 2 hooks.

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 impact

README.md
[![agentmods](https://agentmods.dev/badge/skills/huiruru/ecoguilt/impact.svg)](https://agentmods.dev/skills/huiruru/ecoguilt/impact)
Your own site
<a href="https://agentmods.dev/skills/huiruru/ecoguilt/impact"><img src="https://agentmods.dev/badge/skills/huiruru/ecoguilt/impact.svg" alt="Measured on agentmods" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,849 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00028 $0.01849
Opus 5 $0.00014 $0.00924
Sonnet 5 $0.00006 $0.00370
Haiku 4.5 $0.00003 $0.00185

Measured 5d ago against content hash dedf2b9a42bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

impact 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 5d 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.

skills/impact/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Show the environmental impact of this coding session.

Step 1: Find the session and get the numbers

The status line writes state to /tmp/ecoguilt-{session_id}.json and a per-cwd pointer at /tmp/ecoguilt-cwd-{hash}.txt. Use the pointer to find the correct session for this working directory:

CWD_HASH=$(echo -n "$PWD" | md5 2>/dev/null || echo -n "$PWD" | md5sum 2>/dev/null | cut -d' ' -f1)
SESSION_ID=$(cat "/tmp/ecoguilt-cwd-${CWD_HASH}.txt" 2>/dev/null)
if [ -n "$SESSION_ID" ]; then
  STATE_FILE="/tmp/ecoguilt-${SESSION_ID}.json"
else
  STATE_FILE=$(ls -t /tmp/ecoguilt-*.json 2>/dev/null | grep -v 'models\|test\|recommend\|cwd' | head -1)
  SESSION_ID=$(echo "$STATE_FILE" | sed 's|/tmp/ecoguilt-||; s|\.json||')
fi
echo "STATE: $STATE_FILE"; cat "$STATE_FILE" 2>/dev/null || echo '{}'

If the file is empty or missing, tell the user the status line hasn't recorded data yet.

The file contains: input_tokens, output_tokens, total_tokens, total_kwh, co2_g, water_ml, model, cost_usd, input_kwh_per_token, output_kwh_per_token.

These numbers are the single source of truth — they match what the status line displays. Do NOT recalculate energy, CO2, or water from tokens. Use the values from this file directly.

Step 2: Get Not Diamond's recommendation

Read the cached recommendation using the same session ID from step 1:

cat "/tmp/ecoguilt-${SESSION_ID}-recommend.json" 2>/dev/null || echo '{}'

If the file is empty, missing, or stale, run the recommend script. First find the transcript:

ls -t ~/.claude/projects/*/${SESSION_ID}.jsonl 2>/dev/null | head -1

If that doesn't find a file, try:

ls -t ~/.claude/projects/*/*.jsonl 2>/dev/null | head -1

Then:

bash ${CLAUDE_SKILL_DIR}/../../scripts/recommend.sh "<transcript_path>" 2>/dev/null || echo '{"error": "failed"}'

The response is JSON with model, provider, and messages_sent, or {"error": "no_api_key"} if the user doesn't have NOTDIAMOND_API_KEY set.

Read the full file on GitHub · 142 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. 5d ago First seen · 142 lines · 28 tokens per session scan A dedf2b9a42bb

Subscribe to this mod's changes

impact is a skill published in the GitHub repository huiruru/ecoguilt (9 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 1,849 once invoked, about $0.0001 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 skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens