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 LegalQuants/lq-skills --skill sgcitegit clone --depth 1 https://github.com/LegalQuants/lq-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/legalquants/lq-skills/sgcite)<a href="https://agentmods.dev/skills/legalquants/lq-skills/sgcite"><img src="https://agentmods.dev/badge/skills/legalquants/lq-skills/sgcite/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/legalquants/lq-skills/sgcite"><img src="https://agentmods.dev/badge/skills/legalquants/lq-skills/sgcite.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 | $0.00031 | $0.00938 |
| Opus 5 | $0.00015 | $0.00469 |
| Sonnet 5 | $0.00006 | $0.00188 |
| Haiku 4.5 | $0.00003 | $0.00094 |
Grade A, and why
sgcite 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 12d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sgcite — Singapore Court Citation Checker
When to Use
- Reviewing legal submissions before filing
- Checking AI-generated legal text for hallucinated citations
- Verifying that cited Singapore cases actually exist in eLitigation
- Detecting fabricated case names, paragraph references, or distorted quotations
- Routine citation audit for Singapore law documents
How It Works
Input
A legal text (submission, memo, brief, AI-generated draft) containing Singapore court citations.
Process
- Extract — Parse all case citations from the text
- Validate — Query eLitigation (Singapore's case law database) for each citation
- Check — Detect:
- Hallucinated authorities (case doesn't exist)
- Mismatched case names (case exists but different party names)
- Fabricated paragraph references
- Distorted quotations
- Report — Flag each issue with specific location and explanation
Output
✓ 5 citations verified
✗ 2 issues found:
- [Para 23] "ABC v DEF [2023] SGHC 45" — Case not found in eLitigation
- [Para 41] Quotation distorted — actual text differs from cited passage
Example
npx sgcite check ./my-submission.docx
Or import as a library in your legal workflow tools.
Audience and Work Shape
Audience: Singapore-qualified lawyers, paralegals, and litigation-support teams running a pre-filing or pre-circulation citation check. Not for unsupervised drafting or non-lawyer use.
Work shape: Pattern-Matched Review. Each citation is checked against a known reference (eLitigation). Output is a list of findings, not a sign-off.
Scope and Legal Use
This skill provides legal support, not legal advice. A verified result means "the cited case was found in eLitigation with matching party names" — not "this citation is correctly used in your submission" and not "this submission is fit to file."
Privilege and confidentiality. sgcite sends extracted citation text (and quoted passages, when checking for distorted quotations) to eLitigation. Do not invoke on privileged or unfiled draft material unless your firm has assessed that routing the cited extracts through a public case-law database is acceptable. Configure the tool to send citation strings only (not surrounding context) where possible.
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.
- 12d ago First seen · 94 lines · 31 tokens per session scan A c014aad4434e
sgcite is a skill published in the GitHub repository LegalQuants/lq-skills (54 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 31 tokens to every session and 938 once invoked, about $0.0002 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 2026-08-30.
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