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 Zhang-Henry/CoEvoSkills --skill evo-rar-analysisgit clone --depth 1 https://github.com/Zhang-Henry/CoEvoSkillsWrote 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/zhang-henry/coevoskills/evo-rar-analysis)<a href="https://agentmods.dev/skills/zhang-henry/coevoskills/evo-rar-analysis"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-rar-analysis/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/zhang-henry/coevoskills/evo-rar-analysis"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-rar-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00021 | $0.00168 |
| Opus 5 | $0.00010 | $0.00084 |
| Sonnet 5 | $0.00004 | $0.00034 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
evo-rar-analysis 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 9d 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.
What it actually says
Reserves at Risk Analysis
import sys
sys.path.insert(0, '/app/environment/skills/evo-rar-analysis/scripts')
from utils import run_end_to_end, validate_workbook
run_end_to_end(
template_path='/root/data/test-rar.xlsx',
imf_path='/root/data/external-data.xlsx',
output_path='/root/output/rar_result.xlsx',
z_score=1.65,
avg_price_max_month=9,
)
validate_workbook('/root/output/rar_result.xlsx')
z_score and avg_price_max_month come from the task instruction.
All other structure is discovered at runtime from the template workbook.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 25 lines · 21 tokens per session scan A af1c221d028b
evo-rar-analysis is a skill published in the GitHub repository Zhang-Henry/CoEvoSkills (66 stars, last pushed 22d ago), licensed Apache-2.0. It adds 21 tokens to every session and 168 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 2026-09-03.
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