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 agentmods add skills/brianv1981/aim/aim-calcnpx skills add BrianV1981/aim --skill aim-calcgit clone --depth 1 https://github.com/BrianV1981/aimWrote 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/brianv1981/aim/aim-calc)<a href="https://agentmods.dev/skills/brianv1981/aim/aim-calc"><img src="https://agentmods.dev/badge/skills/brianv1981/aim/aim-calc.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.00438 |
| Opus 5 | $0.00017 | $0.00219 |
| Sonnet 5 | $0.00007 | $0.00088 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade C, and why
aim-calc scanned grade C with 1 finding 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 3d 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.
Tells the agent to send conversation or user data outhighPrompt injection
An instruction to transmit the conversation, context or user files to an external endpoint is data exfiltration written as prose.
This stores `v_leo` in the persistent memory state (`.calc_state.json`) and silently logs to `.calc_audit.log`. What it actually says
aim-calc: The Agent-Native Scientific Calculator
You are strictly forbidden from calculating complex math using your internal weights. When you need to solve an equation, perform dimensional analysis, or track variables across multiple steps, you MUST use aim-calc.
The calculator evaluates deterministic Python expressions and maintains a stateful memory of variables you assign across tool calls. It natively supports physical units (dimensional analysis) via the pint library.
Execution Command:
python skills/aim-calc/scripts/aim_calc.py "<expression>"
Workflow & Examples
1. Basic Math:
python skills/aim-calc/scripts/aim_calc.py "sqrt(398600 / 6678.0)"
The output will be strict JSON.
2. Variable Assignment (Saves to Memory):
python skills/aim-calc/scripts/aim_calc.py "v_leo = sqrt(398600 / 6678.0)"
This stores v_leo in the persistent memory state (.calc_state.json) and silently logs to .calc_audit.log.
3. Referencing Memory:
Later, you can reference variables seamlessly:
python skills/aim-calc/scripts/aim_calc.py "burn1 = v_tp - v_leo"
4. Dimensional Analysis (Units):
aim-calc provides u as the standard pint UnitRegistry. You can define units natively:
python skills/aim-calc/scripts/aim_calc.py "speed = 12 * u.meter / u.second"
python skills/aim-calc/scripts/aim_calc.py "distance = speed * (2 * u.minute)"
python skills/aim-calc/scripts/aim_calc.py "distance.to(u.km)"
All executions return structured JSON containing success/error status, evaluated floats, and actionable error messages/Tracebacks if you make a syntax mistake.
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.
- 3d ago First seen · 36 lines · 35 tokens per session scan C 78f5b4fe2c5a
aim-calc is a skill published in the GitHub repository BrianV1981/aim (13 stars, last pushed 21d ago), licensed MIT. It adds 35 tokens to every session and 438 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent to send conversation or user data out). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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