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-metric-designgit 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-metric-design)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-metric-design"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-metric-design/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-metric-design"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-metric-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00103 | $0.06747 |
| Opus 5 | $0.00051 | $0.03374 |
| Sonnet 5 | $0.00021 | $0.01349 |
| Haiku 4.5 | $0.00010 | $0.00675 |
Grade A, and why
agentsop-metric-design 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 10d 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 — 384 lines — stays where its author put it; the contents beside it link to each section on GitHub.
metric-design — Decomposed, Multi-Criteria Metrics for LLM Pipelines
"It's unproductive to launch optimization runs using a poorly designed program or a bad metric." — DSPy core team [dspy.ai/learn/optimization/overview/]
"LLM judges exhibit self-preference, recency, rubric-order, score-ID, and length biases." — Synthesized from [arxiv.org/pdf/2506.02592, arxiv.org/pdf/2509.26072]
This is a tool skill. It produces a metric function (and a calibration receipt) that other skills consume — DSPy compilers (MIPROv2 / GEPA / BootstrapFewShot), LlamaIndex FaithfulnessEvaluator/RelevancyEvaluator/RetrieverEvaluator, LangGraph eval judges, RAGAS, TruLens. The metric is the optimization target. Get it wrong and every downstream optimizer is theatre.
1. 何时激活 (When to Activate)
Activate when any of these is true:
- Optimization runs: a DSPy / OpenAI-Evals / RAGAS / TruLens job is about to consume a
metric(example, pred) -> bool|float. The metric drives gradient-free search; bias propagates into the artifact. - RAG evaluation: deciding chunk size, reranker, hybrid alpha, retriever-k. A bad metric here picks the wrong chunking strategy and you ship it.
- Agent benchmarks: tool-use, multi-step, planning. A single holistic LLM judge cannot distinguish "wrong tool" from "right tool, wrong args".
- Prompt tuning that has gone past 2 manual iterations: if you're tuning a prompt and have no quantitative metric, you are guessing. Stop the prompt edits, write the metric.
- Production regression test: every chunking / embedding / retriever / prompt PR change needs a metric gate (LlamaIndex
OP-10 EvalLoop).
Do NOT activate for:
- One-off exploratory prompt tests where no decision rides on the output.
- Tasks where exact-match / unit-test / schema-validity already gives ≥95% of signal — don't over-engineer.
- Where the user explicitly refuses to commit to any evaluation criteria (then
dspy-sopwill refuse to compile anyway; this skill cannot help).
What ships with it
4 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.
- 10d ago First seen · 384 lines · 103 tokens per session scan A a6df48eb7e95
agentsop-metric-design is a skill published in the GitHub repository agentsope/SkillAlchemy (385 stars, last pushed 7d ago), licensed MIT. It adds 103 tokens to every session and 6,747 once invoked, about $0.0005 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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