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 launch-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/launch-review)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/launch-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/launch-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/launch-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/launch-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 | $0.00041 | $0.02594 |
| Opus 5 | $0.00020 | $0.01297 |
| Sonnet 5 | $0.00008 | $0.00519 |
| Haiku 4.5 | $0.00004 | $0.00259 |
Grade A, and why
Launch 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 9d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Launch Review
Purpose
Produce a structured, attorney-ready legal issues register for a product or feature before it launches. This skill spots potential legal exposure across multiple practice areas, routes each issue to the right specialist skill or attorney, and frames a draft go / hold / conditions recommendation. It produces draft legal work product for attorney review — not legal advice, not a legal clearance, and not a certification of compliance.
Use When
- A team is preparing to launch a new product, feature, app, or major update and needs a legal issues review before go-live.
- A product manager, engineer, or counsel asks to "do a legal review of this launch," "check if we're good to ship," or "flag legal risks before we release."
- A release date is approaching and legal has not yet reviewed the new functionality.
- A prior launch review exists but material scope has changed (new data types, new markets, new claims, new third-party integrations).
- Post-launch monitoring identifies a gap that requires a retroactive review.
Required Inputs
- Product or feature description: what it does, how users interact with it, and what is new or changed.
- Target markets and users: geographies, user demographics, whether it serves consumers (B2C) or businesses (B2B), and any vulnerable populations (minors, healthcare patients, financial consumers).
- Data collected and processed: types of personal data, sensitive data categories (health, financial, biometric, location, children's data), how data flows, and any third-party data sharing.
- Marketing claims and user-facing representations: copy, screenshots, landing pages, onboarding text, or links to assets.
- Third-party dependencies: APIs, SDKs, open-source libraries, AI model providers, data vendors, or embedded services — with their names and, if known, their license or contract type.
- Launch date: the target date or window.
If any of these inputs are missing, stop and request them before proceeding. Do not fabricate facts, assume data practices, or guess at claims.
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.
- 9d ago First seen · 144 lines · 41 tokens per session scan A a80f1ac6b09e
Launch Review is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 2,594 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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