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/avelikiy/great_ctoWrote 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/agents/avelikiy/great_cto/dpdpa-reviewer)<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.
<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>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 | $0.00086 | $0.01432 |
| Opus 5 | $0.00043 | $0.00716 |
| Sonnet 5 | $0.00017 | $0.00286 |
| Haiku 4.5 | $0.00009 | $0.00143 |
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.
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 fromarchetype-review-base. This prompt adds ONLY the DPDPA / India heuristics.
Domain triggers (in addition to the base "when invoked")
jurisdiction: inin 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.
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.
- 5d ago Changed e3092b5af7a7
- 12d ago First seen · 127 lines · 86 tokens per session scan A 30bd88d3a7e9
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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