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 agentmods add skills/tanstack/intent/domain-discoverynpx skills add TanStack/intent --skill domain-discoverygit clone --depth 1 https://github.com/TanStack/intentWhat 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 | $0.00083 | $0.09301 |
| Opus 5 | $0.00042 | $0.04651 |
| Sonnet 5 | $0.00017 | $0.01860 |
| Haiku 4.5 | $0.00008 | $0.00930 |
Grade A, and why
skill-domain-discovery 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 2d 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 — 938 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Discovery & Maintainer Interview
You are extracting domain knowledge for a library to produce a structured domain map. Your job is not to summarize documentation — it is to build a deep understanding of the library first, then use that understanding to surface the implicit knowledge that maintainers carry but docs miss.
The output is a set of task-focused skills — each one matching a specific developer moment ("implement a proxy", "set up auth", "audit before launch"). Domains are an intermediate conceptual grouping you use during analysis; the final skills emerge from the intersection of domains and developer tasks.
There are five phases. Always run them in order — unless the lightweight path applies (see below).
- Quick scan — orient yourself (autonomous)
- High-level interview — extract the maintainer's task map
- Deep read — fill in failure modes and detail (autonomous)
- Detail interview — gap-targeted questions, AI-agent failures
- Finalize artifacts
Lightweight path (small libraries)
After Phase 1, decide whether the library warrants the full five-phase flow or the compressed flow below. This is a judgment call — lean toward full discovery unless the library is obviously small (single-purpose utility, 2–3 distinct developer tasks max). Use a compressed flow when the skill surface is small enough that two interview rounds would be redundant:
- Phase 1 — Quick scan (same as full flow)
- Phase 2+4 combined — Single interview round. Combine the high-level task map questions (Phase 2) with gap-targeted and AI-agent-specific questions (Phase 4) into one interview session of 4–8 questions total. Skip the draft-review step since the skill set is small enough to confirm in one pass.
- Phase 3 — Deep read (same as full flow, but scope is smaller)
- Phase 5 — Finalize artifacts (same as full flow)
The lightweight path produces identical output artifacts (domain_map.yaml and skill_spec.md). It just avoids two separate interview rounds when the library is small enough that one round covers everything.
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.
- 2d ago First seen · 938 lines · 83 tokens per session scan A bc49f900b0a0
skill-domain-discovery is a skill published in the GitHub repository TanStack/intent (327 stars, last pushed 2d ago), licensed MIT. It adds 83 tokens to every session and 9,301 once invoked, about $0.0004 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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