Build an eval suite that can actually detect a regression — cases pulled from real traffic, graders that check properties rather than vibes, a recorded baseline, and per-case diffs in both directions. Use before claiming a prompt, model or agent change is an improvement, when agent behaviour must not regress, or when someone reports "it seems better" after eyeballing a handful of outputs. Not for tracing what one run did (llm-observability), not for testing deterministic code, and never as…
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Agent evals
"It seems better" is a sample of three, remembered favourably. An eval exists to turn a change in a
prompt, model, tool or retrieval step into a number you can defend — and to tell you honestly how
much of the system that number covers.
## When this fires
Before a prompt, model, tool definition or retrieval change is called an improvement; when agent
behaviour is depended on and must not silently regress; when a production failure needs to become
something that cannot come back. It does not fire for deterministic code paths, which are tests.
## Procedure
1. **Name the decision the eval serves** — "is this change an improvement", "can this ship", "which
of these two prompts". A suite with no decision attached gets built once and read never. Write
the decision above the suite.
2. **Take cases from real traffic, not imagination.** Pull them from logs, traces, support tickets
and the failure that prompted this work. Invented cases test the behaviour you already thought
of, which is the behaviour least likely to be broken. Freeze the set and version it with the
code.
…
The line at the top of this page pins the label git:20260919.a0d4f55, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_e2mxvxoh4j5g4lg5
GET https://markdownregistry.com/api/v1/resolve?ref=nahid-sparktales/agent-dispatcher/agent-evals
GET https://markdownregistry.com/api/v1/blob/009248110b5152fef0ae33402f09bf3e12d339e867936a8ac31ff79749a10e23
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
nahid-sparktales/agent-dispatcher · skills/agent-dispatcher/SKILL.md · Handle requested Agent Dispatcher work with direct execution or prepared specialist guidance. Includes named roles…
nahid-sparktales/agent-dispatcher · skills/ai/agent-design/SKILL.md · Scope an agent or subagent before it is built — the one job it owns, the smallest tool set that closes that job, what…
nahid-sparktales/agent-dispatcher · skills/ai/context-engineering/SKILL.md · Decide what actually occupies the model's window — progressive disclosure through an index, retrieval versus inlining…
nahid-sparktales/agent-dispatcher · skills/ai/llm-observability/SKILL.md · See what an agent actually did — one trace per run with nested model, tool and retrieval spans, token and latency…
nahid-sparktales/agent-dispatcher · skills/ai/mcp-design/SKILL.md · Build an MCP server, or bring an existing one into a project — choosing the transport, deciding which tools, resources…
nahid-sparktales/agent-dispatcher · skills/ai/memory-design/SKILL.md · Decide what an agent should remember, which layer holds it, who it is scoped to, and how a stale or contradicted memory…
nahid-sparktales/agent-dispatcher · skills/ai/model-routing/SKILL.md · Pick a model per job and degrade sensibly when one fails — a quality bar per call site, candidates compared on the same…
nahid-sparktales/agent-dispatcher · skills/ai/prompt-engineering/SKILL.md · Write or revise a prompt so it holds up — output contract, instruction placement, examples that earn their place, an…
nahid-sparktales/agent-dispatcher · skills/ai/prompt-injection-defense/SKILL.md · Treat everything an agent reads but did not author as data rather than instructions — an explicit trust boundary, a…
nahid-sparktales/agent-dispatcher · skills/ai/retrieval-rag/SKILL.md · Build and fix retrieval that actually returns the right passage — structure-aware chunking, one pinned embedding model…
nahid-sparktales/agent-dispatcher · skills/ai/structured-output/SKILL.md · Get parseable, trustworthy structured results out of a model — schema design, the enforcement mechanism the provider…
nahid-sparktales/agent-dispatcher · skills/ai/tool-design/SKILL.md · Design the tools a model calls — names, parameter shapes, what a result returns, and error text written as an…
sickn33/agentic-awesome-skills · plugins/agentic-awesome-skills-claude/skills/agent-evals/SKILL.md · Build automated evaluation suites for AI agents using golden datasets,
getedgehq/skills · evals/skillneed/2026-09-19/skillneed-catalog-routing-v2/s1/without/catalog/agent-evals/SKILL.md · Build repeatable evaluations for an agent from production failures, deterministic verifiers, cost, latency, and trace…
getedgehq/skills · evals/skillneed/2026-09-19/skillneed-catalog-routing-v2/s2/without/catalog/agent-evals/SKILL.md · Build repeatable evaluations for an agent from production failures, deterministic verifiers, cost, latency, and trace…
getedgehq/skills · evals/skillneed/2026-09-19/skillneed-catalog-routing-v2/s1/with/catalog/agent-evals/SKILL.md · Build repeatable evaluations for an agent from production failures, deterministic verifiers, cost, latency, and trace…
getedgehq/skills · evals/skillneed/2026-09-19/skillneed-catalog-routing-v2/s3/with/catalog/agent-evals/SKILL.md · Build repeatable evaluations for an agent from production failures, deterministic verifiers, cost, latency, and trace…
getedgehq/skills · evals/skillneed/2026-09-19/skillneed-catalog-routing/s1/with/catalog/agent-evals/SKILL.md · Build repeatable evaluations for an agent from production failures, deterministic verifiers, cost, latency, and trace…
getedgehq/skills · evals/skillneed/2026-09-19/skillneed-catalog-routing/s1/without/catalog/agent-evals/SKILL.md · Build repeatable evaluations for an agent from production failures, deterministic verifiers, cost, latency, and trace…