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 DedeGroup/listinggood-skills --skill listinggood-amazon-bullet-writergit clone --depth 1 https://github.com/DedeGroup/listinggood-skillsWrote 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/dedegroup/listinggood-skills/listinggood-amazon-bullet-writer)<a href="https://agentmods.dev/skills/dedegroup/listinggood-skills/listinggood-amazon-bullet-writer"><img src="https://agentmods.dev/badge/skills/dedegroup/listinggood-skills/listinggood-amazon-bullet-writer/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/dedegroup/listinggood-skills/listinggood-amazon-bullet-writer"><img src="https://agentmods.dev/badge/skills/dedegroup/listinggood-skills/listinggood-amazon-bullet-writer.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 | $0.00151 | $0.01578 |
| Opus 5 | $0.00076 | $0.00789 |
| Sonnet 5 | $0.00030 | $0.00316 |
| Haiku 4.5 | $0.00015 | $0.00158 |
Grade A, and why
listinggood-amazon-bullet-writer 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 12d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
亚马逊五点描述生成器 — 把特性变成销量
Generate high-converting Amazon bullet points using benefit-driven copywriting frameworks that turn product features into customer "must-have" reasons.
When to Use
Trigger when the user asks to:
- Write or create Amazon bullet points (key product features)
- Optimize existing bullets for better conversion
- Turn a feature list into benefit-led selling copy
- Make bullet points more persuasive or professional
- Apply proven bullet point formulas to their product
- Understand why their bullets aren't converting
Workflow
Step 1: Gather Product Inputs
Collect from user (ask if not provided):
| Field | Required | Notes |
|---|---|---|
| Product name / what it is | ✅ | Core identity |
| Key features (3–8) | ✅ | Raw feature list to transform |
| Target audience | ✅ | Who buys this and why they care |
| Category | ✅ | Affects tone and emphasis |
| Pain points it solves | Optional | What problem does it eliminate? |
| Competitor weaknesses | Optional | Where do others fail? |
| Existing bullets (if optimizing) | Optional | Current text to improve |
Step 2: Choose Bullet Framework
Select the best framework for the product type:
| Framework | Header Style | Best For |
|---|---|---|
| BENEFIT-LED (default) | [Benefit Name] — [Feature → Proof] | Most products — leads with customer value |
| PROBLEM-SOLUTION | [Pain Point Solved] — [How + Result] | Problem-aware audiences (tools, appliances) |
| SENSORY/EXPERIENTIAL | [Experience] — [Sensory description] | Lifestyle, apparel, home decor |
| SPECIFICATION-FIRST | [Spec Category] — [Number + What It Means] | Electronics, technical products |
| TRUST-BASED | [Trust Signal] — [Proof + Benefit] | New brands, premium products |
Default to BENEFIT-LED unless the product clearly fits another framework.
Step 3: Write Each Bullet (5 Total)
For each of 5 bullets, apply this structure:
**BOLD ALL-CAPS HEADER (2–6 words)** — Body text starting with the feature,
then explaining the specific customer benefit, then including a concrete proof
point (number, material, certification, or result).
What ships with it
3 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.
- 12d ago First seen · 143 lines · 151 tokens per session scan A d8d536fc19a7
listinggood-amazon-bullet-writer is a skill published in the GitHub repository DedeGroup/listinggood-skills (1 stars, last pushed 7d ago), licensed MIT. It adds 151 tokens to every session and 1,578 once invoked, about $0.0008 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 2026-08-31.
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