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.
npx skills add zgbrenner/agentcounsel --skill data-retention-schedule-reviewgit clone --depth 1 https://github.com/zgbrenner/agentcounselWrote 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/skills/zgbrenner/agentcounsel/data-retention-schedule-review)<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.
<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>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 | $0.00049 | $0.03231 |
| Opus 5 | $0.00024 | $0.01615 |
| Sonnet 5 | $0.00010 | $0.00646 |
| Haiku 4.5 | $0.00005 | $0.00323 |
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.
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.mdif 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.
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.
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.
- 7d ago First seen · 141 lines · 49 tokens per session scan A 17209fa798dc
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.
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