create-evals
Skill1 starsChanged 13 days agoMITView on GitHub
Generate diverse evaluation scenarios that test whether an agent using a skill produces correct output, including happy-path cases, edge cases, and failure scenarios across varying complexity levels. Produces structured scenario directories containing task prompts, weighted scoring checklists, and optional input fixtures for each scenario. Use when the user says "create evals", "generate eval scenarios", "write evals", "test this skill", or before publishing a skill.
5 other projects use a version of this.
Adopted by
Most recently taken up by
- agno-agi/agentos-dockertheir own version, first seen Oct 30, 2026
- agno-agi/agentos-flytheir own version, first seen Oct 30, 2026
- agno-agi/agentos-rendertheir own version, first seen Oct 30, 2026
- agno-agi/agentos-railwaytheir own version, first seen Oct 29, 2026
- agno-agi/agentos-helmtheir own version, first seen Oct 23, 2026
Used with
Projects with it also use improve.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
AnasX7Skillimprove
Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on source code — never implements, fixes, or refactors anything itself. Use when asked to audit a codebase, find improvement opportunities (bugs, security, performance, test coverage, tech debt, migrations, DX), suggest features or where to take the project next (roadmap, product direction), or generate handoff plans for another agent to implement.
Also in 4 projects that use this
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 5
- agno-agi/agentos-railway★ 41same name
- agno-agi/agentos-docker★ 14same name
- agno-agi/agentos-render★ 5same name
- agno-agi/agentos-fly★ 4same name
- agno-agi/agentos-helm★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository20 other files it gives its agents.
- AGENTS.mdAGENTS.md
- openkata-eval-runnerSkill
- openkata-optimize-skillsSkill
- openkata-review-ruleSkill
- openkata-review-skillSkill
- openkata-rule-conventionsSkill
- openkata-ryu-releaseSkill
- openkata-skill-conventionsSkill
- commit-conventionsSkill
- create-adrSkill
- create-profileSkill
- create-ruleSkill
- create-skillSkill
- critical-thinkingSkill
- github-create-prSkill
- git-worktreeSkill
- grill-meSkill
- grill-with-docsSkill
- makefile-conventionsSkill
- spec-workflowSkill
The file
We split this skill into the rules it gives an agent
9 rules. 0 are common, 7 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
7
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 71
Add "include": ["./inputs"] when input fixtures exist.
- Very specific: line 81
Default is "sandbox": true (requires Docker).
- Distinctive: line 128
Never reference the skill being tested
- Distinctive: line 129
Keep focused on the skill's domain
- Distinctive: line 135
Keep minimal — just enough to make the scenario concrete
- Distinctive: line 136
Must be realistic (real-looking code, configs, docs)
- Distinctive: line 159
Does NOT run evals or judge output
- Distinctive: line 160
Does NOT modify the skill itself
- Distinctive: line 161
Does NOT publish or deploy
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Create Evals
Generate diverse eval scenarios for a skill. Each scenario tests whether an agent following the skill produces the expected output given a realistic task.
Workflow
- Identify target — Determine which skill to create evals for from the user's request.
- Read the skill — Read the skill's SKILL.md and any
references/to understand what behaviors it enforces. - Plan scenarios — Design 3–5 diverse scenarios:
- At least one straightforward happy-path case
- At least one edge case or tricky situation
- Vary complexity (simple, moderate, challenging)
- Cover different aspects of the skill's workflow
- Create scenario directories — One directory per
scenario in the skill's
evals/directory. Use descriptive kebab-case names:skill-name/ └── evals/ ├── scenario-happy-path/ ├── scenario-edge-case/ └── scenario-complex/ - Write files — Each scenario directory contains:
scenario.json— Metadata (see structure below)criteria.json— Weighted scoring checklisttask.md— Realistic task promptinputs/— Optional fixture files
- Verify — Confirm all criteria weights sum to exactly
100 per scenario. Run the validation script:
./scripts/check-eval-weights.sh skills/<name> - Report — List the scenarios created with descriptions.
File Formats
scenario.json
{
"description": "Brief description of the scenario"
}Add "include": ["./inputs"] when input fixtures exist.
For conversational skills (no tool use), add:
{
"description": "Brief description",
"sandbox": false
}Default is "sandbox": true (requires Docker). Set to
false for skills that only produce text output.
criteria.json
{
"context": "What the scenario tests",
"type": "weighted_checklist",
"checklist": [
{
"name": "criterion-name",
"description": "Observable behavior to verify",
"max_score": 15
}
]
}task.md
## Problem/Feature Description
Realistic scenario description.
## Output Specification
What files or artifacts the agent must produce.Criteria Rules
- Weights MUST sum to exactly 100 per scenario
- Weight guide:
- 12–15: Core skill behaviors
- 10–12: Important explicitly-emphasized rules
- 6–8: Supporting rules that matter but aren't the focus
- 4–6: Minor style or formatting details
- Each description must be specific enough for binary pass/fail scoring — no subjective criteria
Task Rules
- Two sections: Problem/Feature Description and Output Specification
- Self-contained and realistic — a believable scenario
- Never reference the skill being tested
- Keep focused on the skill's domain
Input Files
- Optional — only include when the scenario needs existing project context
- Keep minimal — just enough to make the scenario concrete
- Must be realistic (real-looking code, configs, docs)
Example Scenario
For a commit-conventions skill:
evals/
└── scenario-breaking-change/
├── scenario.json
├── criteria.json
├── task.md
└── inputs/
└── diff.patchWhere criteria.json might weight "includes breaking
change footer" at 15, "imperative mood header" at 12,
"scope present" at 10, etc., summing to 100.
Boundaries
- DOES generate eval scenario files
- Does NOT run evals or judge output
- Does NOT modify the skill itself
- Does NOT publish or deploy
Common Failures
- Weights not summing to 100 — always verify arithmetic before writing criteria.json.
- Criteria too vague — "well-written commit message" is unjudgeable. "Header uses imperative mood" is binary.
- Task leaks skill name — the task must not hint at which skill is active or what behaviors are expected.
- Scenarios all test the same aspect — cover different parts of the workflow, not variations of one case.
- Unrealistic inputs — fixture files must look like real project artifacts, not toy examples.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

