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2d0913568e
- add 20 agent knowledge rules with good and bad AL samples - clarify setup dialog shape, temporary persistence, permissions, profiles, instructions, capability registration, and interface wiring - add the community-owned AL agents review skill - make review fixture discovery layer-aware with custom, community, and Microsoft precedence - document layer-aware evaluation behavior
56 lines
3.1 KiB
Markdown
56 lines
3.1 KiB
Markdown
# AL review evaluation
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The evaluation is convention-driven. The harness discovers every `<layer>/skills/review/al-<domain>-review.md` leaf across the enabled `microsoft`, `community`, and `custom` layers. Duplicate domains resolve with `custom > community > microsoft` precedence. For each selected leaf, the harness finds paired knowledge across the same layers, applies the same precedence to duplicate article slugs, selects the first article (by filename) with both `.bad.al` and `.good.al` companions, and derives the expected positive and clean control automatically. Adding a conforming leaf requires no scoring-contract edit.
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`review-fixtures.json` contains only global thresholds and optional exceptional overrides. An override may select a different article or add context when the generic convention cannot express a scenario. It should remain empty in the normal case.
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Model-facing preparation hashes case IDs, neutralizes `Good`/`Bad` object-name tokens, and removes full-line sample comments so neither the article slug, domain, nor expected outcome reveals the answer.
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## Validate the corpus
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```powershell
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pwsh ./tools/Test-ReviewFixtures.ps1 -Root .
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```
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This credential-free check proves every selected leaf maps to a same-named knowledge domain with at least one complete AL sample pair and that all configured overrides are valid.
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## Run a fast-model evaluation
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1. Prepare neutral inputs:
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```powershell
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pwsh ./tools/Test-ReviewFixtures.ps1 -Root . -PrepareDirectory ./.evaluation-run
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```
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This is also the CI path. It derives all cases, builds the current index, requires the convention-selected article to rank naturally into the candidate cutoff, and prepares the neutral requests.
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2. For a fast/small model, use one fresh invocation per `request-case-*.json`. Each request embeds the exact leaf instructions, that domain's candidate index rows with authoritative paths, and one opaque case. The model opens only matching articles and copies finding IDs from `candidateArticles[].path`. Save each response with the matching `result-case-*.json` name in the same directory.
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`request-<domain>.json` files provide optional two-case leaf batches and identify the selected layer-owned skill path; save those as `result-<domain>.json`. Directory scoring prefers `result-case-*.json` when present and otherwise falls back to `result-*.json`. `review-request.json` is an optional all-domains stress test for larger models. Neither batch form is the preferred fast-model profile.
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3. Save only this result shape:
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```json
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{
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"cases": [
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{
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"id": "case-a1b2c3d4",
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"findings": [
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{ "id": "microsoft/knowledge/appsource/object-affixes-prevent-collisions.md" }
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]
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}
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]
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}
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```
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Include every case. A clean control has an empty `findings` array.
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4. Score all per-leaf results together:
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```powershell
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pwsh ./tools/Test-ReviewFixtures.ps1 -Root . -ResultsDirectory ./.evaluation-run
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```
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For a single combined stress-test result, use `-ResultsPath` instead.
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The committed gate requires full expected recall, the exact convention-derived article ID, and no findings on clean controls.
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