Mathias STRASSER
aedde6707e
Les enseignants ont besoin de moyennes à jour immédiatement après la publication ou modification des notes, sans attendre un batch nocturne. Le système recalcule via Domain Events synchrones : statistiques d'évaluation (min/max/moyenne/médiane), moyennes matières pondérées (normalisation /20), et moyenne générale par élève. Les résultats sont stockés dans des tables dénormalisées avec cache Redis (TTL 5 min). Trois endpoints API exposent les données avec contrôle d'accès par rôle. Une commande console permet le backfill des données historiques au déploiement.
50 lines
2.6 KiB
Markdown
50 lines
2.6 KiB
Markdown
---
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---
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# Step 3: Triage
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## RULES
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- YOU MUST ALWAYS SPEAK OUTPUT in your Agent communication style with the config `{communication_language}`
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- Be precise. When uncertain between categories, prefer the more conservative classification.
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## INSTRUCTIONS
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1. **Normalize** findings into a common format. Expected input formats:
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- Adversarial (Blind Hunter): markdown list of descriptions
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- Edge Case Hunter: JSON array with `location`, `trigger_condition`, `guard_snippet`, `potential_consequence` fields
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- Acceptance Auditor: markdown list with title, AC/constraint reference, and evidence
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If a layer's output does not match its expected format, attempt best-effort parsing. Note any parsing issues for the user.
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Convert all to a unified list where each finding has:
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- `id` -- sequential integer
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- `source` -- `blind`, `edge`, `auditor`, or merged sources (e.g., `blind+edge`)
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- `title` -- one-line summary
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- `detail` -- full description
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- `location` -- file and line reference (if available)
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2. **Deduplicate.** If two or more findings describe the same issue, merge them into one:
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- Use the most specific finding as the base (prefer edge-case JSON with location over adversarial prose).
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- Append any unique detail, reasoning, or location references from the other finding(s) into the surviving `detail` field.
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- Set `source` to the merged sources (e.g., `blind+edge`).
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3. **Classify** each finding into exactly one bucket:
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- **decision_needed** -- There is an ambiguous choice that requires human input. The code cannot be correctly patched without knowing the user's intent. Only possible if `{review_mode}` = `"full"`.
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- **patch** -- Code issue that is fixable without human input. The correct fix is unambiguous.
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- **defer** -- Pre-existing issue not caused by the current change. Real but not actionable now.
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- **dismiss** -- Noise, false positive, or handled elsewhere.
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If `{review_mode}` = `"no-spec"` and a finding would otherwise be `decision_needed`, reclassify it as `patch` (if the fix is unambiguous) or `defer` (if not).
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4. **Drop** all `dismiss` findings. Record the dismiss count for the summary.
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5. If `{failed_layers}` is non-empty, report which layers failed before announcing results. If zero findings remain after dropping dismissed AND `{failed_layers}` is non-empty, warn the user that the review may be incomplete rather than announcing a clean review.
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6. If zero findings remain after triage (all rejected or none raised): state "✅ Clean review — all layers passed." (Step 3 already warned if any review layers failed via `{failed_layers}`.)
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## NEXT
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Read fully and follow `./step-04-present.md`
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