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 Incept5/eve-skillpacks --skill eve-agent-optimisationgit clone --depth 1 https://github.com/Incept5/eve-skillpacksWrote 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/incept5/eve-skillpacks/eve-agent-optimisation)<a href="https://agentmods.dev/skills/incept5/eve-skillpacks/eve-agent-optimisation"><img src="https://agentmods.dev/badge/skills/incept5/eve-skillpacks/eve-agent-optimisation/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/incept5/eve-skillpacks/eve-agent-optimisation"><img src="https://agentmods.dev/badge/skills/incept5/eve-skillpacks/eve-agent-optimisation.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 | $0.00053 | $0.02451 |
| Opus 5.5 | $0.00021 | $0.00980 |
| Sonnet 5.5 | $0.00011 | $0.00490 |
| Haiku 4.5 | $0.00005 | $0.00245 |
Grade A, and why
eve-agent-optimisation 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 2mo 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eve Agent Optimisation
The goal: get the agent to its objective in the fewest tool calls, fewest tokens, shortest time. Find where it wastes effort and eliminate it.
Hard Rule: Recommend, Don't Change
Never change the harness, model, reasoning effort, or permission policy without asking the user first. These are cost and capability decisions that belong to the project owner. Diagnose, explain the tradeoff, and recommend — then wait for approval.
What You're Looking For
Analyse agent execution logs to identify:
- Wrong turns — agent tried an approach that couldn't work and had to backtrack.
- Blind alleys — agent spent tokens exploring something irrelevant to the goal.
- Unnecessary tool calls — agent read files it didn't need, ran commands that gave no useful information, or repeated calls with slight variations.
- Missing context — agent had to discover something through trial and error that should have been stated in the SKILL.md or job description.
- Wrong tool for the job — agent used a slow or fragile tool when a faster/native alternative exists (e.g., shelling out to
pdftotextwhen the LLM reads PDFs natively). - Excessive reading — agent read entire large files when it only needed a section, or read many files looking for something that could have been found with a targeted search.
- Verbose output — agent explained its reasoning at length when the task only needed a concise result.
- Retry loops — agent repeated the same failing operation, hoping for a different result.
Diagnostic Workflow
Step 1: Get the Execution Record
eve job diagnose <job-id> # Full timeline, routing, errors
eve job show <job-id> --verbose # Phase, attempts, harness, agent
eve job receipt <job-id> # Token usage + cost
Key numbers:
- Input tokens — how much the agent read. High = reading too much.
- Output tokens — how much it wrote. High = verbose or excessive reasoning.
- Attempt count — more than 1 means the agent crashed or timed out.
- Duration — compare against what a focused agent should take.
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.
- 2mo ago First seen · 227 lines · 53 tokens per session scan A 6d54256496f6
eve-agent-optimisation is a skill published in the GitHub repository Incept5/eve-skillpacks (0 stars, last pushed 4mo ago), licensed MIT. It adds 53 tokens to every session and 2,451 once invoked, about $0.0002 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 2026-08-31.
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