Data Retention Schedule Review

Data Retention Schedule Review is a skill for Claude Code, Codex from zgbrenner/agentcounsel. It costs 49 tokens per session (3,231 once invoked), scanned A, original, MIT.

A review of a data retention schedule, which says what kinds of data an organization keeps and for how long. It checks the schedule's stated purposes, deletion steps, legal holds, vendors, and backups for gaps.

In plain words
What is it for?
It helps review retention schedules before adoption or periodic updates and prepare questions for legal or litigation counsel.
Why use it?
It helps uncover missing coverage, contradictions, and conflicts that could affect how data is kept or deleted.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps review retention schedules before adoption or periodic updates and prepare questions for legal or litigation counsel.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zgbrenner/agentcounsel/data-retention-schedule-review
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.

Any agent
npx skills add zgbrenner/agentcounsel --skill data-retention-schedule-review
Clone the repo
git clone --depth 1 https://github.com/zgbrenner/agentcounsel

Made for: Claude Code, Codex.

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 Data Retention Schedule Review

README.md
[![agentmods](https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/data-retention-schedule-review/github.svg)](https://agentmods.dev/skills/zgbrenner/agentcounsel/data-retention-schedule-review)
Your own site
<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/data-retention-schedule-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/data-retention-schedule-review/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 Data Retention Schedule Review

Your own site · 80×15
<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/data-retention-schedule-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/data-retention-schedule-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,231 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 $0.00049 $0.03231
Opus 5 $0.00024 $0.01615
Sonnet 5 $0.00010 $0.00646
Haiku 4.5 $0.00005 $0.00323

Measured 7d ago against content hash 17209fa798dc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

Data Retention Schedule Review 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 7d 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.

skills/privacy/data-retention-schedule-review/SKILL.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Retention Schedule Review

Purpose

Organize a review of a draft or existing data retention schedule into an attorney-ready working file. The skill inventories the data categories the schedule covers, maps each to its stated business, legal, or regulatory purpose, collects the retention-period facts as written, and surfaces where the schedule's mechanics — legal-hold overrides, deletion and anonymization steps, vendor and backup coverage — create gaps or contradictions. It produces draft legal work product for attorney review — not legal advice.

This skill never determines whether a stated retention period is legally sufficient, excessive, or compliant with any law. Retention-period adequacy varies by jurisdiction, sector, and data category, and is always an attorney-verification item. The skill also never resolves a legal-hold conflict; it identifies where the schedule and a hold obligation may collide and routes that collision to litigation or legal-hold counsel.

Use When

  • The organization has a draft retention schedule and wants it checked for internal consistency and completeness before adoption.
  • An existing retention schedule needs a periodic review against the current data inventory.
  • A privacy, records-management, or legal-ops team wants retention periods, legal bases, and deletion mechanics organized for attorney sign-off.
  • A DSAR, audit, or regulatory inquiry has surfaced a question about how long a data category is actually kept versus what the schedule says.
  • Counsel needs to understand how a proposed litigation hold or preservation notice would interact with the standing retention schedule.

Required Inputs

  • The retention schedule itself — the draft or existing document, spreadsheet, or policy text, provided in full. Do not review from a description alone.
  • The data inventory or record of processing activities, if one exists, so categories in the schedule can be checked against what the organization actually processes. If none is available, note the gap — this review cannot independently verify that the schedule is complete against actual data holdings.
  • The stated purpose for each retention period — business need, legal/regulatory requirement, or contractual obligation, as the organization or the schedule states it. Do not supply a purpose the skill infers from category name alone.
  • Optional: known legal holds or preservation obligations currently in effect, so hold-override interactions can be flagged.
  • Optional: vendor and backup system inventory — third-party processors, backup and archive systems, and disaster-recovery copies that may hold the same data categories outside the primary system.
  • Optional: the practice group's practice-profiles/privacy.md if populated and loaded alongside this skill. If present, use its Standard Positions and Escalation Thresholds to benchmark the review; if absent, proceed without profile benchmarking.

Read the full file on GitHub · 141 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 7d ago First seen · 141 lines · 49 tokens per session scan A 17209fa798dc

Subscribe to this mod's changes

Data Retention Schedule Review is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 3,231 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 2026-09-03.

Related

Other skills, from other repositories

data-policy

Use when building internal data-governance machinery: a retention schedule (period, lawful basis, expiry action, system where deletion runs), an Art. 6 lawful-basis register, an Art. 30 ROPA, or a consent capture/withdrawal model. NOT the public privacy notice or DSAR handling (that is gdpr-privacy), NOT SOC 2 posture…

ericrisco/rsc-harness · 88 tokens

legal-risk-checker

Reviews a story brief, draft article, or broadcast script and flags potential legal risks — including defamation, privacy, contempt of court, and data protection — so you know what to check before publication.

ur-grue/autopunk-media-skills · 45 tokens

gdpr-data-handling-note

Drafts a plain-language data handling notice for a journalistic or media production project that involves collecting, storing, or processing personal data — structured to meet GDPR transparency requirements while remaining understandable to non-lawyers.

ur-grue/autopunk-media-skills · 48 tokens

gdpr-note-writer

Drafts a GDPR compliance note for a specific piece of journalistic content or data collection activity — documenting the lawful basis for processing personal data, what data is held, how long it is retained, and who has access.

ur-grue/autopunk-media-skills · 50 tokens

implementing-gdpr-data-subject-access-request

Automates GDPR Data Subject Access Request (DSAR) workflows including identity verification, PII discovery across databases and files using regex and NER, data mapping, response templating per Article 15 requirements, deadline tracking, and audit logging. Covers ICO/EDPB guidance compliance, exemption handling, and…

ThomasMoreAI/legal-skills-open · 90 tokens

ai-data-privacy

Reviews AI/ML systems for data privacy and governance risks including training data privacy, PII exposure in prompts and completions, data retention policies, model memorization risks, and regulatory compliance. Auto-invoked when reviewing systems that process personal data through LLMs, train or fine-tune models on…

UnitOneAI/SecuritySkills · 106 tokens