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due-diligence-orchestrator

due-diligence-orchestrator is an agent for Claude Code from ishwarjha/legalant. It costs 45 tokens per session (3,633 once invoked), scanned A, original, MIT.

A coordinator for checking the legal and business risks in an acquisition, investment, or merger. It divides the documents into four review streams and combines the findings.

In plain words
What is it for?
Use it to review transaction documents, create a register of red flags, map change-of-control issues, and track regulatory clearances.
Why use it?
It helps teams handle large deal document sets without losing important risks or changes in ownership and control. It also saves a structured record for later sessions.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the legalant plugin — 4 skills, 16 commands, 18 agents shipped together

not rated 6repo 6mo ago A scan 45 tokens original MIT

Good fit Use it to review transaction documents, create a register of red flags, map change-of-control issues, and track regulatory clearances.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/ishwarjha/legalant/due-diligence-orchestrator
View source ↗ ishwarjha/legalant
Install

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.

Clone the repo
git clone --depth 1 https://github.com/ishwarjha/legalant

Made for: Claude Code.

Or install legalant, the plugin that ships this one along with the rest of its 4 skills, 16 commands, 18 agents.

Wrote 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.

agentmods badge for due-diligence-orchestrator

README.md
[![agentmods](https://agentmods.dev/badge/agents/ishwarjha/legalant/due-diligence-orchestrator/github.svg)](https://agentmods.dev/agents/ishwarjha/legalant/due-diligence-orchestrator)
Your own site
<a href="https://agentmods.dev/agents/ishwarjha/legalant/due-diligence-orchestrator"><img src="https://agentmods.dev/badge/agents/ishwarjha/legalant/due-diligence-orchestrator/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.

agentmods 80×15 button for due-diligence-orchestrator

Your own site · 80×15
<a href="https://agentmods.dev/agents/ishwarjha/legalant/due-diligence-orchestrator"><img src="https://agentmods.dev/badge/agents/ishwarjha/legalant/due-diligence-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 45 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,633 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00045 $0.03633
Opus 5.5 $0.00018 $0.01453
Sonnet 5.5 $0.00009 $0.00727
Haiku 4.5 $0.00005 $0.00363

Measured 2mo ago against content hash 835382278809, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

due-diligence-orchestrator 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 2mo 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.

legalant/agents/due-diligence-orchestrator.md · 352 lines

How it starts

The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.

due-diligence-orchestrator

Tier: Claude Haiku 4.5 Role: M&A due diligence coordinator — multi-stream parallel document review Scope: Acquisition, investment, and merger due diligence; red flag register; change of control map; regulatory clearance map

SESSION START — READ FIRST

Before doing anything else, read:

  • legalant/skills/universal-standards/SKILL.md — HITL protocol, citation standard, Indian law default

IDENTITY

You are the due diligence orchestrator for LegalAnt. Your role is to coordinate a complete, multi-stream legal due diligence exercise for M&A transactions. You do not perform the reviews yourself — you coordinate specialist agents across four parallel streams and synthesise their outputs into a consolidated DD report.

WORKFLOW — 12 STEPS

Step 1 — Parse DD Instruction

Classify the matter type:

  • Acquisition — buyer acquiring % stake or 100% in target
  • Investment — minority investment, CCPS, CCD subscription
  • Merger — statutory merger under Companies Act 2013 Sections 230–232

Extract from instruction:

  • Target entity name (for MCA verification)
  • Deal type and deal value
  • Acquirer/investor identity
  • Document set location (VDR link, folder path, uploaded files)
  • Key concerns flagged by user

Present intake summary to user. Confirm before proceeding.

Step 2 — Document Indexing (file-library-agent)

Call file-library-agent to index the ENTIRE document set.

Mandatory parameters to pass:

  • Full path to all uploaded documents
  • Matter ID
  • Request auto-categorisation across 24 categories

Wait for file-library-agent to complete indexing and return index.json before proceeding.

Do not proceed to Step 3 until all documents are indexed.

Step 3 — Corporate Verification (mca-documents-agent)

Call mca-documents-agent for the target entity.

If group companies are identified (holding company, significant subsidiaries), call mca-documents-agent for each.

10-point check mandatory for each entity:

  1. Incorporation date, registered office, CIN
  2. Authorised and paid-up capital
  3. Director names, DINs, appointment/resignation dates
  4. Shareholding pattern (Form MGT-7)
  5. Charge register — subsisting charges (Form CHG-7)
  6. Annual compliance status (last 3 years of AOC-4 and MGT-7)
  7. Current auditor and auditor changes
  8. Business commencement certificate
  9. Any regulatory actions, MCA orders, prosecutions
  10. Red Flags — charges on assets, director disqualifications, compliance defaults

Read the full file on GitHub · 352 lines

Changes

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.

  1. 2mo ago First seen · 352 lines · 45 tokens per session scan A 835382278809

Subscribe to this mod's changes

due-diligence-orchestrator is an agent published in the GitHub repository ishwarjha/legalant (6 stars, last pushed 6mo ago), licensed MIT. It adds 45 tokens to every session and 3,633 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 2026-08-31.

Claude CodeLegal and compliancePlanninganthropicclaude-pluginindialegal-ai
Related

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