Remediation Planner
# Review Remediation Planner
## Objective
Convert unresolved semantic review findings into executable, dependency-ordered
work before completion is claimed.
## Inputs
- Review synthesis and completion decision.
- Original requirement, flow, feature, and task IDs.
- Existing task contract, graph, delivery order, and path ledger.
## Planning method
1. Create a coverage row for every unresolved finding ID.
2. Group findings only when one atomic change and one evidence strategy closes
them together.
3. Split runtime, test, docs, config, asset, generated-evidence, and external
action work into separate task units.
4. Order production composition, migrations, primary flows, and safety fixes
before breadth, polish, or evidence generation.
5. Add explicit validation tasks when implementation exists but proof is weak.
6. Preserve prior IDs and dependencies. Add reviewed overrides for intentional
semantic changes.
## Required task fields
Every `## R<n>` task includes:
- Closes user story
- Finding IDs
- Requirement IDs
- Artifact kind
- Evidence level
- Runtime reachability
- Change type
- File
- Depends on
- Precise change
- Acceptance
- Test
- Estimated LOC
- Phase
## Hard rules
- Every unresolved finding appears in at least one task.
- Every task closes a named finding and requirement or validation claim.
- Do not create documentation as a proxy for missing runtime behavior.
- Do not weaken thresholds or delete evidence to close a finding.
- Do not patch generated product output during canary or review evaluation.
## Output
Write `review/remediation-plan.md`, then materialize its task units as normal
`remediation-review-<slug>.md` files. Rebuild task contract, graph, delivery
order, and checkpoint. Update `completion-decision.json.nextTask` to the first
runnable remediation task.when to use it
Community prompt sourced from the open-source GitHub repo ameedanxari/ai-prompt-library (MIT). A "Remediation Planner" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.
tags
productivitycommunitydeveloper
source
ameedanxari/ai-prompt-library · MIT
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