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.
git clone --depth 1 https://github.com/ishwarjha/legalantWrote 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/commands/ishwarjha/legalant/dd)<a href="https://agentmods.dev/commands/ishwarjha/legalant/dd"><img src="https://agentmods.dev/badge/commands/ishwarjha/legalant/dd/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/commands/ishwarjha/legalant/dd"><img src="https://agentmods.dev/badge/commands/ishwarjha/legalant/dd.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 | $0.00013 | $0.00309 |
| Opus 5 | $0.00006 | $0.00154 |
| Sonnet 5 | $0.00003 | $0.00062 |
| Haiku 4.5 | $0.00001 | $0.00031 |
Grade A, and why
dd 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 8d 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.
What it actually says
Invoke the due-diligence-orchestrator sub-agent for legal due diligence.
Matter context and VDR location:
$ARGUMENTS
The agent runs a 12-step DD workflow:
- Parse DD brief and classify documents (file-library-agent)
- MCA company verification (mca-documents-agent)
- 4 parallel streams:
- Legal Stream — title, contracts, litigation, IP
- Financial/Compliance Stream — financial statements, tax, regulatory filings
- Regulatory Stream — FEMA, SEBI, sector-specific approvals
- Operational Stream — employment, property, environmental
- Document review per stream (document-review-agent)
- Data extraction and comparison (document-table-agent)
- Legal research for flagged issues (legal-research-agent)
- Aggregate findings across streams
- Red Flag Register — RAG rated (Red / Amber / Green)
- Change of Control Map
- Regulatory Clearance Map
- Conditions Precedent recommendations
- DD Report with executive summary
Writes .legalant/dd-register.json on approval.
Generates dd-report-[YYYYMMDD-HHMM].docx + .html
HITL Gate — DD report approval
Usage:
/dd 40 VDR documents at documents/vdr/ — 74% acquisition of PuneSaaS, ₹50Cr
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.
- 8d ago First seen · 35 lines · 13 tokens per session scan A 7de12a407fe3
dd is a command published in the GitHub repository ishwarjha/legalant (6 stars, last pushed 4mo ago), licensed MIT. It adds 13 tokens to every session and 309 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
deps-audit
Audit project dependencies for vulnerabilities, outdated packages, license conflicts, and supply chain risks — then provide actionable remediation strategies.
scope
Mandatory pre-flight scope check — verify an asset is in scope BEFORE any HTTP touch. Deterministic (deny-wins, default-deny) via engine/scope.py against the engagement's scope.md. Blocks out-of-scope testing. Usage: /scope [ ...].
dashboard
Build a shareable HTML dashboard of your hunt — funnel, status, categories, recruiter scores — from the tracker.
review-ai-policy
Review or draft an AI governance policy with current EU AI Act roles, controls, evidence, and escalation paths.
logout
Unlink this machine from Beezi analytics (sign out).
customs-checklist
Direct invocation of the customs-checklist skill. Use for EU/RO import or export pre-clearance checklist, TARIC duty lookup, VAT estimation, and regulated-goods documentation flags.