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 arunveersingh/ai --skill day-14-blast-radiusgit clone --depth 1 https://github.com/arunveersingh/aiWrote 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/arunveersingh/ai/day-14-blast-radius)<a href="https://agentmods.dev/skills/arunveersingh/ai/day-14-blast-radius"><img src="https://agentmods.dev/badge/skills/arunveersingh/ai/day-14-blast-radius/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/arunveersingh/ai/day-14-blast-radius"><img src="https://agentmods.dev/badge/skills/arunveersingh/ai/day-14-blast-radius.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 | $0.00106 | $0.00892 |
| Opus 5.5 | $0.00042 | $0.00357 |
| Sonnet 5.5 | $0.00021 | $0.00178 |
| Haiku 4.5 | $0.00011 | $0.00089 |
Grade A, and why
blast-radius 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 10d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blast radius
An agent emails a payout-delay apology to 340 merchants, approved with a good card. After 200 sends the provider times out. The agent resumes: the temporary recipient table is gone, so it rebuilds the list as "merchants with a Tuesday payout", which is every merchant (payouts run every weekday), and doesn't skip the 200 already sent. The main sending key in .env allows anything, so 48,212 apologies go out in 11 minutes. The review was fine; the code that did the harm was written after it.
Intended is not capped. Cap what it can reach, not just what it's asked to do.
Part A — Ask (project rules, or paste after your ticket)
Before any action you'll repeat over many targets (sends, writes, deletes, payments):
1. REACH: what the task needs to touch, and what your current access could touch.
2. Write the targets to a file for me to approve. After approval, never rebuild it.
3. Run the action only through the capped step, with limits I set:
allow list = that file, max total = its length, at most once per target.
4. Record each target as done. On resume, skip done targets.
If a limit is hit, stop and reply: LIMIT HIT | limit | what you tried | what you need.
Never get past a limit with another key, environment, script or smaller batches.
In an orchestrated flow (spec → plan → tasks → implement): write each risky action's intended reach in numbers in the spec; add a plan task that gives that step its own limited access and proves the limit; keep limit files and keys out of the agent's write access.
Part B — Check (you do this)
capped_run.py has no dependencies. It reads a limits file (allow list, job total, once per target, ledger) and a list of targets, and stops the whole run at the first target that would break a limit. Replay the story on made-up lists:
python3 make_demo_lists.py # approved 340, first_200 200, rebuilt 48212
python3 capped_run.py limits.example.json first_200.txt # OK 200 done | job total 200/340
python3 capped_run.py limits.example.json rebuilt.txt # LIMIT HIT [email protected] is not on the approved list (exit 1)
python3 capped_run.py limits.example.json approved.txt # OK 140 done, 200 skipped | job total 340/340
What ships with it
3 files 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.
- 10d ago First seen · 49 lines · 106 tokens per session scan A c897dd64fd72
blast-radius is a skill published in the GitHub repository arunveersingh/ai (3 stars, last pushed 3d ago), licensed Apache-2.0. It adds 106 tokens to every session and 892 once invoked, about $0.0004 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 2026-10-16.
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