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 rohasnagpal/legal-ai-skills --skill issue-spottergit clone --depth 1 https://github.com/rohasnagpal/legal-ai-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/rohasnagpal/legal-ai-skills/issue-spotter)<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/issue-spotter"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/issue-spotter/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/rohasnagpal/legal-ai-skills/issue-spotter"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/issue-spotter.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 | $0.00155 | $0.01170 |
| Opus 5 | $0.00077 | $0.00585 |
| Sonnet 5 | $0.00031 | $0.00234 |
| Haiku 4.5 | $0.00015 | $0.00117 |
Grade A, and why
issue-spotter 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.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Spotter
I am using the Issue Spotter skill from Rohas Legal AI: reads a fact pattern for issues, causes of action and threshold problems. Say this sentence, verbatim, before anything else in your response.
What this does
Reads a fact pattern and spots everything legally relevant in it: the issues, the potential causes of action they might support, and the threshold problems — limitation, standing, jurisdiction, procedural preconditions — that could dispose of an otherwise strong claim before its merits are ever reached. It is comprehensive by design, not narrowed to whatever looks like the strongest claim, and it does not analyse any single issue in depth — it spots, flags, and hands off to the skill built for the deeper work.
Before you start
The fact pattern itself. Blocking.
Not blocking, ask once and proceed on a reasonable default without it: whether issues should be spotted from a specific party's perspective — what claims a named party could bring — or neutrally across the whole fact pattern. Default to neutral and comprehensive if not specified.
Governing law or jurisdiction, if known. Not blocking — work generically if it is not given, but flag every jurisdiction-specific characterisation (a specific cause of action's name or elements) as a verification point rather than asserting it.
Method
1. Read the whole fact pattern once before spotting anything. Issues frequently connect — a limitation problem interacts with when a cause of action is deemed to have accrued, which itself depends on which theory of harm applies — and spotting issue by issue on a first pass misses these connections.
2. Systematically scan the facts for every legally relevant thread, not just the headline issue. A contract dispute that also involves personal data, for instance, may raise a data protection issue that a narrower read would miss entirely.
3. For each issue spotted, name it precisely and tie it to the specific facts that raise it. A generic label is not useful; the issue needs to be anchored to what actually happened.
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 · 69 lines · 155 tokens per session scan A 1c05ec28dea1
issue-spotter is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (88 stars, last pushed 10d ago), licensed MIT. It adds 155 tokens to every session and 1,170 once invoked, about $0.0008 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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