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.
npx skills add tmusser/ai-engineering-skills --skill eco-modegit clone --depth 1 https://github.com/tmusser/ai-engineering-skillsWrote 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.
[](https://agentmods.dev/skills/tmusser/ai-engineering-skills/eco-mode)<a href="https://agentmods.dev/skills/tmusser/ai-engineering-skills/eco-mode"><img src="https://agentmods.dev/badge/skills/tmusser/ai-engineering-skills/eco-mode/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tmusser/ai-engineering-skills/eco-mode"><img src="https://agentmods.dev/badge/skills/tmusser/ai-engineering-skills/eco-mode.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.00885 |
| Opus 5.5 | $0.00011 | $0.00354 |
| Sonnet 5.5 | $0.00005 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
eco-mode 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eco Mode
Purpose
Make bounded agent work economical through source selection, fresh-context discipline, and a measurable preflight. This is an opt-in working-set policy, not a second workflow, model switch, or replacement for verification.
When to use
Use when the operator invokes eco mode, asks to minimize context costs, or runs a bounded task on a fast inexpensive model. Especially useful for maintenance, well-scoped coding, extraction, and small-model delegated work.
Do not force it on ambiguous research, incident response, high-stakes decisions, or tasks whose required evidence cannot fit. Escalate when necessary.
Inputs
- Objective, current source of truth, scope and acceptance/proof targets
- Only the selected relevant code/docs and durable state files
- Optional provider/model choice outside this skill
- Local selected-content budget, default 60,000 UTF-8 bytes
Workflow
- Resolve the route first, or honor an already explicit route. Do not append ceremony to a sufficient workflow.
- Load only the current authority-bearing task sources, relevant file sections, and next proof target. Search and expand when a named risk warrants it.
- Optionally use existing context hydration for Markdown:
python scripts/aes.py context "bounded task" --strict > /tmp/eco.md. Inspect the packet status and required-file coverage before relying on it. - Check the actual selected text files before sending them:
python scripts/aes.py eco /tmp/eco.md, or pipe the selected prompt topython scripts/aes.py eco -. The default combined ceiling is 60,000 bytes; REVIEW_REQUIRED means prune redundancy or explicitly expand. - Execute one slice with existing scope controls and verification. Avoid redundant tool-output envelopes and needless refetches. Reuse anchors.
- Preserve required instructions, exact file paths, uncertainties, acceptance criteria, failed tests, compatibility checks, and verification evidence. Never abbreviate a failing proof into apparent success.
- On scope drift, missing evidence, or budget pressure, retrieve the smallest missing authoritative source. Use a trusted handoff into a fresh context, or switch to a more capable model when required. Never silently truncate.
- Stop after required proof. Measure actual provider token usage and spend where available; the byte checker cannot estimate billed usage.
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.
- 7d ago First seen · 89 lines · 27 tokens per session scan A 8c8781cf29a4
eco-mode is a skill published in the GitHub repository tmusser/ai-engineering-skills (5 stars, last pushed 7d ago), licensed MIT. It adds 27 tokens to every session and 885 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-10.
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