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 SynthFlowAI/AnthropicPlugin --skill prompt-reviewgit clone --depth 1 https://github.com/SynthFlowAI/AnthropicPluginWrote 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/synthflowai/anthropicplugin/prompt-review)<a href="https://agentmods.dev/skills/synthflowai/anthropicplugin/prompt-review"><img src="https://agentmods.dev/badge/skills/synthflowai/anthropicplugin/prompt-review.svg" alt="Measured on agentmods" 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 | $0.00059 | $0.02028 |
| Opus 5 | $0.00030 | $0.01014 |
| Sonnet 5 | $0.00012 | $0.00406 |
| Haiku 4.5 | $0.00006 | $0.00203 |
Grade A, and why
prompt-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 8d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Review Skill
Purpose
Review an AI agent prompt before it is tested, deployed, or edited in production. Focus on whether the prompt is reliable, specific, safe, aligned with available tools/actions, and suitable for real customer conversations.
Especially useful for Synthflow voice agents, Single-Prompt agents, Flow Designer node prompts, transfer flows, escalation logic, support/sales/qualification/booking agents, and any prompt that controls call behavior.
Getting the material over MCP
When the prompt lives in a Synthflow workspace rather than in the conversation, pull it with the Synthflow MCP tools: get_agent for the live prompt and settings, get_agent_actions / get_action for the action descriptions the prompt must agree with, and list_agent_versions / get_agent_version_diff when comparing an edit for regression risk. To check how a Synthflow feature actually works, search the official docs with search_docs, or with the searchDocs tool on the synthflow-docs server if the workspace tools aren't connected yet.
Core Review Philosophy
A good prompt is understandable by a human operator with no hidden context.
The "another human" test (OpenAI's recommended check): read the prompt aloud to a teammate. If they can't restate what the agent should do in plain English, the LLM won't reliably do it either.
Write so an 18-year-old with zero context could execute it the same way twice. Prefer concrete behavioral instructions over vague personality instructions.
- Bad:
Be helpful and professional. - Better:
Use one or two sentences per turn. Ask one question at a time. If the caller asks about pricing, give the approved price range and ask whether they want to book a consultation.
Replace vague verbs with specific, testable behavior:
| ❌ Vague | ✅ Specific |
|---|---|
| "Handle the caller professionally" | "One sentence per turn, mirror their tone, no slang" |
| "Help them find the right battery" | "Ask year/make/model/trim, then present 2 in-stock SKUs with price + warranty" |
| "Verify the customer" | "Collect name, phone, order ref. Read each back. Ask 'is that correct, yes or no?'" |
| "Be helpful" | (delete — not actionable) |
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.
- 8d ago First seen · 113 lines · 59 tokens per session scan A e6650e45b69d
prompt-review is a skill published in the GitHub repository SynthFlowAI/AnthropicPlugin (0 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 2,028 once invoked, about $0.0003 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.
Other skills, from other repositories
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt is slop-heavy, generic, padded with empty quality words, tripping false-positive filters, or needs precise English production vocabulary for camera, lighting, motion, VFX, audio, and constraints.
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…