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 agentsope/SkillAlchemy --skill agentsop-domain-eval-setgit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote 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/agentsope/skillalchemy/agentsop-domain-eval-set)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-domain-eval-set"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-domain-eval-set/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/agentsope/skillalchemy/agentsop-domain-eval-set"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-domain-eval-set.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 435 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00098 | $0.06448 |
| Opus 5.5 | $0.00039 | $0.02579 |
| Sonnet 5.5 | $0.00020 | $0.01290 |
| Haiku 4.5 | $0.00010 | $0.00645 |
Grade A, and why
agentsop-domain-eval-set 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 1mo 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 — 436 lines — stays where its author put it; the contents beside it link to each section on GitHub.
domain-eval-set — Your Held-Out Domain Benchmark
"Compiled program beats baseline on a held-out test set (not the val set used in optimization)." — DSPy SOP exit criterion [dspy.ai/learn/optimization/overview/]
"Build the eval loop before optimizing anything. Every subsequent change must be gated on these numbers." — LlamaIndex SOP Stage 2
This is an ENHANCE overlay skill. It produces one artifact — a versioned,
sealed, human-labeled set of 50–200 examples drawn from your domain — that
other skills consume: [[agentsop-regression-gate]] enforces it on every PR,
[[agentsop-metric-design]] defines the scoring function applied to each example, and
[[lm-evaluation-harness]] runs the complementary public-capability axis. The
core claim: public benchmarks tell you the model is smart in general; only a
held-out domain set tells you it works on your task. The latter is the one that
predicts production.
1. 何时激活 (When to Activate)
Activate when any of these is true:
- "Does THIS system work on OUR data?" — someone is about to ship or trust an LLM/RAG/agent system and the only evidence is vibes, a demo, or a public benchmark number. You need a quantitative answer on the real distribution.
- A public-benchmark number is being used as a deployment gate. Someone cites "92% on MMLU" or "passes HumanEval" to justify go-live. That measures general capability, not your task fit (AP-1). Force a domain set into the decision.
- A model / prompt / retriever / chunking change needs a regression gate and
no domain test set exists yet to gate against. You must build the set before
[[agentsop-regression-gate]]can do its job. - Switching models (GPT-4o → a cheaper or local model). The public-bench gap may be small while the domain gap is large, or vice versa. Only your held-out set tells you which.
- Production complaints don't match your eval scores. Either the set is stale (refresh, OP-DE06) or it never reflected the domain (rebuild from real traffic).
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.
- 1mo ago First seen · 436 lines · 98 tokens per session scan A d74a74f6bb52
agentsop-domain-eval-set is a skill published in the GitHub repository agentsope/SkillAlchemy (445 stars, last pushed 14d ago), licensed MIT. It adds 98 tokens to every session and 6,448 once invoked, about $0.0004 per session on Opus 5.5. 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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