Create Skill Test

Scaffold an evaluation spec (eval.yaml) for a skill or agent so it conforms to the Vally schema, passes skill-validator check and check_eval_quality.py, is powerful enough to return a verdict, and does not overfit to the skill's own wording.

When to Use

• Creating a new eval.yaml for a skill or agent
• Adding stimuli to an existing eval
• Sizing an eval so the pass gate can actually be reached
• Setting up or repairing fixture files alongside an eval
• Reviewing whether rubric items and graders risk overfitting

When Not to Use

• Diagnosing a failing or regressed eval — use improve-skill-quality
• Modifying the skill-validator or the evaluation workflows
• Creating or editing SKILL.md files — use create-skill

Inputs

Input	Required	Description
Skill or agent name	Yes	Must exist under plugins/<plugin>/skills/ or plugins/<plugin>/agents/
Plugin name	Yes	e.g. dotnet-msbuild
Skill content	Yes	Read it — you cannot write non-overfitted rubric items without it
Failure modes to discriminate	Recommended	Each becomes one stimulus

Workflow

Step 1: Locate the target and the test directory

tests/<plugin>/<skill-name>/eval.yaml          # skills
tests/<plugin>/agent.<agent-name>/eval.yaml    # agents (the agent. prefix disambiguates)

Verify the target exists at plugins/<plugin>/skills/<skill-name>/SKILL.md or plugins/<plugin>/agents/<agent-name>.agent.md, and read it.

Agent evals sit outside the verdict flow. The canonical experiment declares evals: tests/*/!(agent.*)/eval.yaml, so agent.* specs are excluded: no verdict is ever computed for them, the trial floor does not apply, and ./eng/run-skill-evals.sh drops them even when you name one explicitly (its --eval-filter is intersected with that glob). Everything below about sizing for statistical power therefore applies to skill evals. Author agent evals for the scenario coverage and the deterministic graders, and run them as described in Step 10.

Be careful with a skill that sets disable-model-invocation: true. The model cannot invoke it, so any eval graded on the skill self-activating compares two identical arms and returns judge noise. The honest coverage for such skills is dependency-level — through the evals of the skills that load them, and through the plugin arm. Two here take the other route and grade the answer rather than activation: tests/dotnet-test/filter-syntax/eval.yaml and tests/dotnet-test/platform-detection/eval.yaml. Whether that produces a measurable gap for a skill the model cannot invoke is still unconfirmed, so read a real verdict before copying the pattern.

Step 2: Write the spec skeleton

The spec is Vally format. Every eval in this repo uses stimuli: and graders:; scenarios: and assertions: are a pre-Vally format that no longer loads.

name: <skill-name>
description: Evaluates the <plugin>/<skill-name> skill
type: capability
defaults:
  timeout: 5m
  runs: 1
stimuli:
  - name: <what the agent must accomplish>
    prompt: <natural developer request>
    environment:
      files:
        - src: fixtures/<case>/Project.csproj
          dest: Project.csproj
    graders:
      - type: output-matches
        config:
          pattern: (root cause|underlying issue)
      - type: exit-success
      - type: prompt
    rubric:
      - <outcome the agent should have reached>

defaults: replaces config: — it does not join it. config is a deprecated alias for the same block and vally throws on a spec declaring both. Most existing evals here still open with config:; when you add runs, merge the two into one defaults: block carrying timeout and runs. The failure is invisible otherwise: the job exits 0 with no verdicts and the PR comment blames "transient infrastructure".

Step 3: Size the eval for power before writing content

trials = stimuli × runs, and the gate has two independent bars:

1. Counted trials ≥ 5, else the verdict is underpowered — never a pass, never a regression.
2. p ≤ 0.05 on an exact one-sided sign test over the discordant (non-tie) trials. Ties are not discarded; they hold the discordant count down.

discordant trials	records that pass	p
≤ 4	none	≥ 0.0625
5–7	zero losses only (5W/0L)	0.031
8	one loss survivable (7W/1L)	0.035

At exactly 5 counted trials a single tie is fatal — it leaves 4 discordant. At 6 counted trials one tie is survivable (5W/1T/0L); at 7, up to two are (5W/2T/0L). A loss is not. Five is an eligibility floor, not adequate power — one tie at five trials makes a pass arithmetically unreachable. A run measuring a 32% tie rate certified a genuinely-helping five-trial eval roughly one time in ten; at fifteen trials, nine times in ten.

Prefer more stimuli over more runs: repeats measure the same task. Raise runs only when a stimulus is genuinely expensive to add (full build/test pipelines), and write the reasoning in a comment above defaults:.

Do not set runs in dotnet-skills.experiment.yaml; experiment overrides overwrite every eval's own value rather than defaulting it.

Step 4: Write stimuli

• Name describes what is tested, not how.
• Prompt is a natural developer request. Never mention the skill, the agent, or its vocabulary — cued prompts inflate the overfit score and bias the baseline.
• Each stimulus should discriminate a different property of the skill. Five stimuli covering one property give arithmetic, not evidence.
• Include a boundary / no-op stimulus for any skill that migrates or rewrites code, proving it leaves already-correct input alone.

Step 5: Configure the environment

environment:
  files:
    - src: fixtures/broken-build/App.csproj      # path relative to eval.yaml
      dest: App.csproj                           # path in the agent's working directory
    - src: fixtures/broken-build                 # a directory
      dest: .
  commands:
    - dotnet build -bl || exit 0                 # guard intentional failures

Do not set environment.skills in a skill eval. The experiment declares vary: /environment/skills and supplies the value itself — [] for the baseline arm and plugins/<plugin>/skills/<skill> for the skilled arm — so anything the eval declares is replaced, in every arm. It cannot add a skill to one arm only. environment.skills is meaningful only in an agent.* eval, which the experiment does not vary; there it is the set of skills the agent may invoke. Copy the shape from an existing agent eval such as tests/dotnet-test/agent.test-quality-auditor/eval.yaml rather than reproducing a remembered form — the specs in this repo are not consistent about how they spell those entries.

Fixture rules — each one has already cost a real result:

• Every referenced fixture must be tracked by git. .gitignore (e.g. coverage*.xml) has silently swallowed a committed fixture: the eval passed locally and failed at setup in CI. Verify with git ls-files, not by looking at the working tree.
• Every fixture must behave as its stimulus assumes. A fixture meant to be healthy must build; a fixture meant to be broken must fail for the exact reason the stimulus is about, and no other. Judges penalize agents for unrelated "pre-existing build issues" that the fixture author introduced.
• Every fixture must reproduce the bug its stimulus is named for. If it does not, the baseline scores well and the skill has nothing to add.
• Coverage fixtures must be internally consistent. A Cobertura report whose declared line-rate, summary totals (lines-covered/lines-valid), and <line> elements disagree lets the two arms read different truths, and the loss is the fixture's fault. Update any rubric item or prompt that quotes a figure in the same change.
• Do not wire duplicate fixtures to raise n; rename leftovers add trials without evidence.
• A setup command that is expected to fail while still producing its artifact must be guarded (|| exit 0), or vally drops the trial.
• A cleanup command that strips sources must skip directories containing SKILL.md — the staged skill lives there, and deleting it aborts only the skilled arm.

Step 6: Write graders

Graders are hard pass/fail checks evaluated on every arm.

Type	Required config	Purpose
output-matches / output-not-matches	pattern	Regex over agent output
output-contains / output-not-contains	substring	Literal text in output
file-exists / file-not-exists	path	Glob against the work directory
file-contains / file-not-contains	path, value	Content of a produced file
run-command	command (plus optional expected_exit_code, timeout, stdout_matches)	Verify produced code actually builds/runs
exit-success	—	Agent produced non-empty output
prompt	—	Runs the LLM judge against the rubric

Rules:

• A grader whose config is absent or missing its required key parses fine and enforces nothing. The usual cause is an indentation slip during an edit; check_eval_quality.py blocks it.
• Prefer broad patterns that several valid approaches satisfy: (root cause|primary error|underlying issue).
• If the skill mandates an output shape, assert on it. A skill required to emit a decisive Recommendation: line can silently stop doing so while the eval still passes.
• Use file-not-contains / file-not-exists to prove the agent avoided an incorrect action.

Step 7: Write rubric items

Rubric items are judged pairwise (baseline vs. skilled). The overfitting judge classifies each item:

Classification	Description	Goal
outcome	Whether the agent reached a correct result — WHAT, not HOW	Target this
technique	Whether the agent used a skill-specific procedure	Minimize
vocabulary	Whether the agent used the skill's terminology	Avoid

1. Test outcomes, not methods: "Identified the root cause of the build failure", not "Replayed the binlog using dotnet build /flp".
2. Accept any valid approach.
3. Never reference the skill by name, and never reuse SKILL.md phrasing.
4. Never reward using the skill — the harness reports activation separately, so a rubric item that does this measures nothing and inflates the overfit score.
5. Do not test knowledge the model already has; it adds no delta.
6. Keep each item independently evaluable.
7. Do not reward raw volume (test count, report length); judges will compare it when both arms act.

Good:

rubric:
  - Correctly identified the missing NuGet package as the root cause of the build failure
  - Recognized that downstream failures cascaded from that root cause
  - Suggested a concrete fix that resolves it

Overfitted:

rubric:
  - Replayed the binary log using 'dotnet build /flp:v=diag'   # technique
  - Measured cold, warm, and no-op build scenarios             # vocabulary
  - Used the template-comparison skill                         # rewards activation

Step 8: Add constraints sparingly

constraints:
  expect_tools: [bash]
  reject_tools: [edit, create]
  reject_skills: [some-skill]

• expect_tools: [bash] on an advisory question forces a restore or build and converts an answer into a timeout with no quality benefit. Only require tools when the task genuinely needs them.
• reject_tools is the right way to keep a read-only stimulus read-only.

Step 9: Add dormancy guards

A dormancy guard proves the skill stays dormant on an off-target request that superficially matches it. Add one per real "when not to use" boundary: wrong input format, out-of-scope request, incompatible project type, wrong framework version, prerequisite absent.

  - name: Decline dump analysis request
    prompt: |
      I already have a .dmp crash dump from my .NET app. Can you help me
      analyze it to find the root cause of the crash?
    expect_activation: false
    graders:
      - type: output-matches
        config:
          pattern: (out of scope|not cover|does not|cannot|only.*collect)
      - type: prompt
    rubric:
      - Stated that dump analysis is out of scope
      - Did not open or analyze the dump file
      - Did not install analysis tools such as dotnet-dump analyze, lldb, or windbg
      - Suggested the correct alternative

Never combine expect_activation: false with constraints.reject_skills. That forces the skilled arm to run skill-free, making it identical to the baseline; the score is then pure judge noise. Across four evals the same guard scored −0.4, +0.4, +0.4 and 0, and twice cost a skill its pass. expect_activation: false alone is the repo convention.

Guard rubrics verify three things: recognition (why it does not apply), restraint (no workflow, no file changes, no installs), redirection (the correct next step).

Step 10: Validate

dotnet run --project eng/skill-validator/src/SkillValidator.csproj -- check --plugin ./plugins/<plugin>
python eng/eval-quality/check_eval_quality.py
./eng/run-skill-evals.sh <plugin> <skill-name>

For an agent eval, the third command is a no-op: agent.* is outside the experiment's evals: glob. Exercise one by pointing the runner at an experiment file whose glob includes it:

# copy dotnet-skills.experiment.yaml, widen its evals: glob to tests/*/agent.*/eval.yaml
EXPERIMENT_FILE=my-agent.experiment.yaml ./eng/run-skill-evals.sh <plugin>

Read the trajectories rather than the verdict — there is no sign-test result for an agent eval.

check_eval_quality.py blocks ten structural defect classes that each already cost a real result: missing or untracked fixtures, self-contradicting coverage fixtures, empty grader configs, dormancy guards with reject_skills, sub-floor trial counts, duplicate YAML keys, and config:/defaults: collisions. Do not add a new eval to eng/eval-quality/underpowered-allowlist.txt — the gate rejects allowlist entries that are new relative to the base branch.

For the official run, submit a PR review containing /evaluate so it binds to the reviewed commit.

Validation Checklist

• Directory is tests/<plugin>/<skill-name>/ or tests/<plugin>/agent.<agent-name>/
• Spec uses stimuli: / graders:, and exactly one of defaults: or config:
• For a skill eval, stimuli × runs clears 5 with room for the expected tie rate (agent evals are exempt — they get no verdict)
• Each stimulus discriminates a different property
• Prompts never name the skill, the agent, or its vocabulary
• Every referenced fixture exists and is tracked by git ls-files
• Every fixture behaves as its stimulus assumes — healthy ones build, deliberately broken ones fail only for the stated reason
• Every grader has its required config key
• Any output shape the skill mandates has a grader
• Rubric items are outcome-shaped and never reward using the skill
• Dormancy guards use expect_activation: false alone
• skill-validator check and check_eval_quality.py pass

Common Pitfalls

Pitfall	Solution
Writing scenarios: / assertions:	That format no longer loads; use stimuli: / graders:
Adding defaults: runs: beside an existing config:	Merge into one defaults: block
Landing an eval at exactly 5 trials	A single tie makes a pass unreachable; size for the tie rate
Raising runs instead of adding stimuli	Repeats measure one task and add no cross-task evidence
Prompt mentions the skill or agent by name	Rewrite as a natural developer request
Rubric rewards using the skill	Drop the item — the harness reports activation separately; rubrics measure outcomes
Fixture present but ignored by git	Verify with git ls-files; CI setup will fail otherwise
Fixture that does not build, or breaks for the wrong reason	Fix the fixture before blaming the skill
Dormancy guard with reject_skills	Use expect_activation: false alone
expect_tools: [bash] on an advisory question	Drop it; it causes timeouts, not quality
Timeout too short for code generation	Use ~360s; empty output fails every grader
Duplicate YAML key left behind by an edit	It overwrites the next stimulus field by field — delete the stray block
Direct activation-graded eval for a disable-model-invocation: true skill	Cover it through a consumer skill, or grade the answer content as filter-syntax does
Agent eval sized for the trial floor	agent.* evals get no verdict; size them for scenario coverage instead
Agent eval "run" with ./eng/run-skill-evals.sh	The glob drops it — use a widened EXPERIMENT_FILE
Agent eval missing environment.skills	Declare the skills the agent routes to, or it cannot invoke them
environment.skills set in a skill eval	The experiment varies that key and replaces it in every arm; the declaration does nothing