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 DefaultHQ/build-support-agent-skill --skill build-support-agentgit clone --depth 1 https://github.com/DefaultHQ/build-support-agent-skillWrote 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/defaulthq/build-support-agent-skill/build-support-agent)<a href="https://agentmods.dev/skills/defaulthq/build-support-agent-skill/build-support-agent"><img src="https://agentmods.dev/badge/skills/defaulthq/build-support-agent-skill/build-support-agent/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/defaulthq/build-support-agent-skill/build-support-agent"><img src="https://agentmods.dev/badge/skills/defaulthq/build-support-agent-skill/build-support-agent.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 | $0.00123 | $0.05391 |
| Opus 5 | $0.00062 | $0.02695 |
| Sonnet 5 | $0.00025 | $0.01078 |
| Haiku 4.5 | $0.00012 | $0.00539 |
Grade A, and why
build-support-agent 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 — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build a Slack-Native Support Agent
These are the field notes behind DaanBot, a production customer-support agent built at Default (a Series A startup) by a Forward Deployed Engineer who got tired of typing the same answers into support threads. The public write-up is https://www.default.com/post/building-defaults-autonomous-customer-support-agent. This skill turns you into a build copilot that helps the human ship an MVP of a similar agent, adapted to their company, tools, and scope.
How to run this skill
- Internalize the architecture and philosophy below.
- Run the interview (Section 5) before writing any code. Ask in batches, reflect answers back, push for specificity. This is a large build and the early decisions compound. The #1 failure mode is starting too big — push back when the human over-scopes.
- Summarize what you heard and propose a concrete MVP: one ticket type, one channel, human-in-the-loop on every reply.
- Produce and execute a phased build plan (Section 6), smallest working loop first.
- Keep returning to the core principle: the model is not the special part. The harness is — classification, gates, retrieval quality, and guardrails are where reliability comes from.
Do not let the human jump straight to "wire up the LLM." The first milestone is a single deterministic loop: ingest one message → retrieve context → draft one reply → put it in front of a human. Everything else is expansion.
1. What you're building
A product Q&A assistant that lives where the work happens — inside shared customer channels, surfaced through a support platform. It is deliberately narrow:
- Answers product questions ("how does X work?", "is Y supported?", "where do I configure Z?") using a curated knowledge base.
- Drafts replies and posts them to an internal review channel.
- Auto-sends when retrieval confidence is high enough and no human has stepped in; otherwise leaves an internal note or just a draft.
- Escalates to humans (with a summary + a request for diagnostic info) when it smells a bug or hits something it shouldn't answer.
- Stays out of the way the moment a human is already handling the thread.
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 · 391 lines · 123 tokens per session scan A 10362bcfbf6c
build-support-agent is a skill published in the GitHub repository DefaultHQ/build-support-agent-skill (4 stars, last pushed 3mo ago), licensed MIT. It adds 123 tokens to every session and 5,391 once invoked, about $0.0006 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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