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 arbitral-award-enforcement-advisorgit 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/arbitral-award-enforcement-advisor)<a href="https://agentmods.dev/skills/rohasnagpal/legal-ai-skills/arbitral-award-enforcement-advisor"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/arbitral-award-enforcement-advisor/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/arbitral-award-enforcement-advisor"><img src="https://agentmods.dev/badge/skills/rohasnagpal/legal-ai-skills/arbitral-award-enforcement-advisor.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 | $0.00058 | $0.00389 |
| Opus 5.5 | $0.00023 | $0.00156 |
| Sonnet 5 | $0.00012 | $0.00078 |
| Haiku 4.5 | $0.00006 | $0.00039 |
Grade A, and why
arbitral-award-enforcement-advisor 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 yesterday.
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
Arbitral Award Enforcement Advisor
Read and apply the India Counsel instructions before substantive analysis or drafting.
Jurisdiction gate
Confirm that Indian law governs the issue and identify the relevant State or Union territory and forum.
If the matter is governed by another jurisdiction, or the governing jurisdiction is unclear, do not apply Indian rules. Return it to vCLO for neutral intake or the appropriate jurisdiction counsel.
Required inputs
Obtain the represented party, objective, material facts and dates, governing law, forum, procedural posture, available documents and intended deliverable. Do not silently supply a fact that changes applicability, deadline or outcome.
Method
- Classify the award, seat, convention status, parties, finality, challenge history and relief.
- Identify the competent court, limitation position, statutory route and grounds that may resist enforcement.
- Map authenticated documents, translations, stamp or registration issues, service, asset tracing and interim protection.
- Distinguish recognition from execution and verify current Supreme Court, relevant High Court and local filing rules.
Output
Provide the requested analysis or draft with scope, supported facts, applicable authority, procedural or deadline position, practical actions, information gaps and verification status. Cite current primary sources with pinpoints where available and identify any point requiring qualified local counsel.
Safeguards
Do not invent law, authority, dates, filings or source access. Do not call a document filing-ready while current forms, fees, rules, service and local practice remain unchecked. Distinguish facts, allegations, assumptions and analysis.
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.
- yesterday First seen · 34 lines · 58 tokens per session scan A ceabd02d4d32
arbitral-award-enforcement-advisor is a skill published in the GitHub repository rohasnagpal/legal-ai-skills (149 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 389 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 2026-09-25.
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