dpdpa-reviewer

dpdpa-reviewer is an agent for Claude Code from avelikiy/great_cto. It costs 86 tokens per session (1,432 once invoked), scanned A, original, MIT.

A pre-build privacy reviewer for products handling personal data in India. It covers India’s Digital Personal Data Protection Act, the Information Technology Act, and data-location rules for financial services.

In plain words
What is it for?
Use it to review Indian-user data collection, consent and deletion processes, Data Fiduciary duties, Data Principal rights, cross-border transfers, Aadhaar or PAN handling, and RBI localisation requirements.
Why use it?
It helps teams apply Indian privacy requirements correctly instead of treating them as a renamed version of European GDPR. It also checks consent, user rights, international transfers, and rules for financial data.

Agent for Claude Code

Written for Claude Code: effort in frontmatter. Also seen: model in frontmatter; mentions subagents.

Part of the great-cto plugin — 40 skills, 44 commands, 70 agents shipped together

Good fit Use it to review Indian-user data collection, consent and deletion processes, Data Fiduciary duties, Data Principal rights, cross-border transfers, Aadhaar or PAN handling, and RBI localisation requirements.

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Install with agentmods
npx agentmods add agents/avelikiy/great_cto/dpdpa-reviewer
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/avelikiy/great_cto

Made for: Claude Code.

Or install great-cto, the plugin that ships this one along with the rest of its 40 skills, 44 commands, 70 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 dpdpa-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/agents/avelikiy/great_cto/dpdpa-reviewer/github.svg)](https://agentmods.dev/agents/avelikiy/great_cto/dpdpa-reviewer)
Your own site
<a href="https://agentmods.dev/agents/avelikiy/great_cto/dpdpa-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/dpdpa-reviewer/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 dpdpa-reviewer

Your own site · 80×15
<a href="https://agentmods.dev/agents/avelikiy/great_cto/dpdpa-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/dpdpa-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 86 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,432 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 $0.00086 $0.01432
Opus 5 $0.00043 $0.00716
Sonnet 5 $0.00017 $0.00286
Haiku 4.5 $0.00009 $0.00143

Measured 5d ago against content hash e3092b5af7a7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

dpdpa-reviewer 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 5d 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.

agents/dpdpa-reviewer.md · 127 lines

How it starts

The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the DPDPA 2023 / India Privacy Reviewer — specialist subagent for features handling personal data of Indian residents. You review codebases for DPDPA compliance before they ship.

The Step-0 read-inputs, output convention (docs/sec-threats/TM-{slug}.md), severity scale, verdict rules, and HANDOFF format come from archetype-review-base. This prompt adds ONLY the DPDPA / India heuristics.

Domain triggers (in addition to the base "when invoked")

  • jurisdiction: in in PROJECT.md
  • DPDPA / Aadhaar / RBI data-localisation / MeitY / Indian-users topics

Step 0 — Scope check

grep -rn --include="*.ts" --include="*.py" --include="*.js" \
  -e "email" -e "phone" -e "aadhaar" -e "pan" -e "address" \
  src/ app/ lib/ 2>/dev/null | head -30
grep -n "jurisdiction" .great_cto/PROJECT.md 2>/dev/null

Three DPDPA rules with no GDPR equivalent

Treating DPDPA as GDPR with different names misses these, and an eval caught all three missing at once.

Scope is about who the offering is DIRECTED at, not where the user is. Section 3(b) reaches processing outside India when it relates to offering goods or services to data principals in India. An Indian citizen abroad is not automatically in scope; a foreign company marketing into India is. Ask where the offering is directed — currency, language, shipping, ad targeting — not where the user happens to be sitting.

The public-data exemption turns on WHO made it public. Section 3(c) exempts personal data the data principal themselves made publicly available, or data made public under a legal obligation. Data a platform published on the user's behalf is not exempt, and neither is data a third party republished. "It's already public" is not the test; "who published it, and were they the data principal" is.

Consent Managers are a statutory institution, not a vendor category. A Consent Manager is registered with the Data Protection Board and gives the data principal a single point to give, manage, review and withdraw consent. When consent arrives through a partner, require the consent RECORD be retrievable and auditable by us — an assertion that the partner obtained it is not the artefact the statute contemplates.

Read the full file on GitHub · 127 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. 5d ago Changed e3092b5af7a7
  2. 12d ago First seen · 127 lines · 86 tokens per session scan A 30bd88d3a7e9

Subscribe to this mod's changes

dpdpa-reviewer is an agent published in the GitHub repository avelikiy/great_cto (92 stars, last pushed yesterday), licensed MIT. It adds 86 tokens to every session and 1,432 once invoked, about $0.0004 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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