Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillnpx agentmods add skills/charlieviettq/awesome-agent-skill/draft-responseWrote 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/charlieviettq/awesome-agent-skill/draft-response)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/draft-response"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/draft-response/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/charlieviettq/awesome-agent-skill/draft-response"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/draft-response.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 | $0.00053 | $0.03232 |
| Opus 5 | $0.00026 | $0.01616 |
| Sonnet 5 | $0.00011 | $0.00646 |
| Haiku 4.5 | $0.00005 | $0.00323 |
Grade A, and why
draft-response 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.
This is a copy
97% identical to draft-response — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/draft-response
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Draft a professional, customer-facing response tailored to the situation, customer relationship, and communication context.
Usage
/draft-response <context about the customer question, issue, or request>
Examples:
/draft-response Acme Corp is asking when the new dashboard feature will ship/draft-response Customer escalation — their integration has been down for 2 days/draft-response Responding to a feature request we won't be building/draft-response Customer hit a billing error and wants a resolution ASAP
Workflow
1. Understand the Context
Parse the user's input to determine:
- Customer: Who is the communication for? Look up account context if available.
- Situation type: Question, issue, escalation, announcement, negotiation, bad news, good news, follow-up
- Urgency: Is this time-sensitive? How long has the customer been waiting?
- Channel: Email, support ticket, chat, or other (adjust formality accordingly)
- Relationship stage: New customer, established, frustrated/escalated
- Stakeholder level: End user, manager, executive, technical, business
2. Research Context
Gather relevant background from available sources:
~~email:
- Previous correspondence with this customer on this topic
- Any commitments or timelines previously shared
- Tone and style of the existing thread
~~chat:
- Internal discussions about this customer or topic
- Any guidance from product, engineering, or leadership
- Similar situations and how they were handled
~~CRM (if connected):
- Account details and plan level
- Contact information and key stakeholders
- Previous escalations or sensitive issues
~~support platform (if connected):
- Related tickets and their resolution
- Known issues or workarounds
- SLA status and response time commitments
~~knowledge base:
- Official documentation or help articles to reference
- Product roadmap information (if shareable)
- Policy or process documentation
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 · 420 lines · 53 tokens per session scan A 32bd2d49edae
draft-response is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 3,232 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to draft-response, differing in 7 lines, and is treated as a copy.
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