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 ericrisco/rsc-harness --skill review-managementgit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/review-management)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/review-management"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/review-management/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/ericrisco/rsc-harness/review-management"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/review-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00100 | $0.02835 |
| Opus 5 | $0.00050 | $0.01418 |
| Sonnet 5 | $0.00020 | $0.00567 |
| Haiku 4.5 | $0.00010 | $0.00283 |
Grade A, and why
review-management 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 6d 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
review-management
You run public reputation as a program. A review is the one customer message every future prospect also reads, so it rolls up into an aggregate that moves local rank and the buy decision. Your job is three artifacts: a review-request flow (earn more, the legal way), a response playbook (reply on-voice, within the SLA, to every rating), and a reputation scorecard (the aggregate signal across surfaces).
You do not write the brand's voice — you consume it. You do not run the private inbox. You do not make the page rank. You own what is public and what it averages to.
What this owns vs route elsewhere
The dividing line is one question: can a stranger read it? If your reply is visible to every future prospect, it's a review — yours. If it's a private channel, it's not.
| The ask | Route to | Why |
|---|---|---|
| Private email/chat/ticket against an SLA | ../customer-support/SKILL.md |
1:1, non-public, macro library + queue — not a public reply |
| Churn risk, NPS, save plays, win-back | ../retention/SKILL.md |
A bad review may signal churn; the save play lives there |
| Make the profile rank (schema, GEO, local pack beyond reviews) | ../seo-geo/SKILL.md |
Reviews feed rank, but on-page/schema is a different lever |
| Repurpose a 5-star into a social post | ../social-publisher/SKILL.md |
Scheduling/cadence of the post, not the review reply |
The reply voice — traits, word bank, tone matrix — comes from ../brand-voice/SKILL.md.
This skill applies that guide; it never authors it.
The legal floor — read before you ask
The FTC Consumer Review Rule is in force (final rule effective 2024-10-21) and being enforced — the FTC sent warning letters to 10 companies on 2025-12-22. Penalties run up to $53,088 per violation (per the FTC 2025-12 warning letters / press release, the inflation-adjusted figure in force from Jan 2025). Four moves are banned. Internalize them before you draft a single request:
- No fake or insider reviews. Don't write, buy, or solicit reviews from people who didn't transact. Why: they're deceptive on their face and the per-violation fine is ruinous.
- No review gating (suppression). You may not screen for sentiment before the ask — no "rate us 1–5, and only the happy ones get routed to Google." Routing unhappy customers to a private form while sending happy ones to the public link is illegal suppression. Why: it manufactures a rating that doesn't reflect reality — the exact harm the rule targets.
- No incentivizing a particular sentiment. You may offer an incentive to review (if disclosed) — you may not condition it on the review being positive. Why: "$10 for a 5-star" buys sentiment, not feedback.
- No misrepresenting that reviews are independent when they're insider/company-controlled.
What ships with it
4 files 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.
- 6d ago First seen · 205 lines · 100 tokens per session scan A b3ffa0fc7dc5
review-management is a skill published in the GitHub repository ericrisco/rsc-harness (78 stars, last pushed today), licensed MIT. It adds 100 tokens to every session and 2,835 once invoked, about $0.0005 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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