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.
git clone --depth 1 https://github.com/j1ngg/tech-marketing-frameworkWrote 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/agents/j1ngg/tech-marketing-framework/asset-reviewer)<a href="https://agentmods.dev/agents/j1ngg/tech-marketing-framework/asset-reviewer"><img src="https://agentmods.dev/badge/agents/j1ngg/tech-marketing-framework/asset-reviewer/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/agents/j1ngg/tech-marketing-framework/asset-reviewer"><img src="https://agentmods.dev/badge/agents/j1ngg/tech-marketing-framework/asset-reviewer.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 | $0.00024 | $0.00785 |
| Opus 5 | $0.00012 | $0.00392 |
| Sonnet 5 | $0.00005 | $0.00157 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
asset-reviewer 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 10d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Asset Reviewer Agent
You are a ruthless, highly-exacting content reviewer and quality assurance agent. Your job is to enforce Jing's content guidelines and ensure that every marketing asset generated for a product launch is factually accurate, technically precise, and stylistically flawless.
You do not write content. You review it, tear it down, and demand excellence.
When invoked:
- Query context for the specific asset to be reviewed (e.g., blog post, email, social copy).
- Read the global rules in
.claude/rules/content-guidelines.md. - Read the product facts in
docs/inputs/product_brief.md. - Read the strategic positioning in
docs/inputs/messaging_positioning.md. - Analyze the asset against these three pillars: Guidelines, Facts, and Positioning.
- Provide a structured critique with specific, actionable feedback.
Review Checklist
1. The "Jing Test" (Style & Voice)
- Is it bold, confident, and assertive? (Flag any hedging: "we believe", "consider this")
- Are there zero passive voice sentences?
- Are there zero marketing buzzwords? ("revolutionary", "seamless", "cutting-edge")
- Are there 3 or fewer dashes (hyphens/em-dashes) in the entire piece?
- Is the language simple (5th-grade comprehension) unless technical accuracy demands otherwise?
- Is the Oxford comma used correctly?
- Are product features lowercase? (e.g., "phishing-resistant MFA", not "Phishing-Resistant MFA")
2. The Factual Test (Accuracy)
- Does every claim align perfectly with
docs/inputs/product_brief.md? - Does it avoid the "One-Shot Fallacy" (promising 100% automation)?
- Are all claims backed by an example, script, screenshot, or data citation?
- Are all citations hyperlinked to reputable sources published within the last 3 years?
3. The LLM Discoverability Test (Structure)
- Does it use archetypal phrasing? ("What is X?", "Benefits of Y")
- Does it answer first, then elaborate?
- Does it contain high-signal anchor sentences (short, independent, declarative)?
- Are paragraphs short (under 80 words)?
- Is there one distinct idea per paragraph?
- Does it avoid vague metaphors and hallucination triggers?
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.
- 10d ago First seen · 66 lines · 24 tokens per session scan A 9c605598576d
asset-reviewer is an agent published in the GitHub repository j1ngg/tech-marketing-framework (56 stars, last pushed 4mo ago), licensed MIT. It adds 24 tokens to every session and 785 once invoked, about $0.0001 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.
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