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Add Copilot Centralization Optimizer workflows (#39394)
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‎.github/aw/actions-lock.json‎

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"version": "v4.36.2",
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"sha": "8aad20d150bbac5944a9f9d289da16a4b0d87c1e"
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},
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"github/gh-aw-actions/setup@v0.79.6": {
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"repo": "github/gh-aw-actions/setup",
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"version": "v0.79.6",
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"sha": "5c2fe865bb4dc46e1450f6ee0d0541d759aea73a"
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},
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"github/gh-aw/actions/setup-cli@v0.79.8": {
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"repo": "github/gh-aw/actions/setup-cli",
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"version": "v0.79.8",

‎.github/workflows/copilot-centralization-drilldown.lock.yml‎

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‎.github/workflows/copilot-centralization-drilldown.md‎

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---
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name: Copilot Centralization Drilldown
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description: Expands one mined centralization candidate into a concrete draft workflow or reusable prompt template.
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on:
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workflow_dispatch:
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inputs:
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candidate_title:
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description: Short name for the candidate skill or repeated task
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required: true
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target_kind:
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description: workflow, shared_prompt, playbook, or auto
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required: false
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default: auto
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recommendation_kind:
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description: Recommendation from the optimizer
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required: false
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default: shared_prompt_or_chatops
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sample_prompt:
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description: Representative prompt text
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required: false
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evidence_summary:
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description: Compact summary of repeated behavior and expected value
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required: false
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candidate_json:
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description: Optional compact JSON object from the optimizer output
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required: false
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permissions:
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contents: read
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copilot-requests: write
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strict: true
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max-ai-credits: 120
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max-daily-ai-credits: 500
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safe-outputs:
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mentions: false
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allowed-github-references: []
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max-bot-mentions: 1
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create-issue:
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title-prefix: "[copilot-centralization-draft] "
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labels: [report, ai-optimization, workflow-design]
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expires: 30
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steps:
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- name: Normalize candidate input
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env:
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CANDIDATE_TITLE: ${{ inputs.candidate_title }}
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TARGET_KIND: ${{ inputs.target_kind }}
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RECOMMENDATION_KIND: ${{ inputs.recommendation_kind }}
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SAMPLE_PROMPT: ${{ inputs.sample_prompt }}
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EVIDENCE_SUMMARY: ${{ inputs.evidence_summary }}
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CANDIDATE_JSON: ${{ inputs.candidate_json }}
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run: |
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set -euo pipefail
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GH_AW_SAFE_OUTPUTS="${GH_AW_SAFE_OUTPUTS:-${RUNNER_TEMP:-/tmp}/gh-aw/safeoutputs/outputs.jsonl}"
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mkdir -p /tmp/gh-aw/data
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if [ -n "${CANDIDATE_JSON}" ]; then
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printf '%s' "$CANDIDATE_JSON" | jq '.' > /tmp/gh-aw/data/candidate-raw.json
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else
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jq -n \
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--arg title "$CANDIDATE_TITLE" \
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--arg recommendation_kind "$RECOMMENDATION_KIND" \
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--arg sample_prompt "$SAMPLE_PROMPT" \
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--arg evidence_summary "$EVIDENCE_SUMMARY" \
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'{
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title: $title,
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recommendation_kind: $recommendation_kind,
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sample_prompt: (if $sample_prompt == "" then null else $sample_prompt end),
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evidence_summary: (if $evidence_summary == "" then null else $evidence_summary end)
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}' > /tmp/gh-aw/data/candidate-raw.json
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fi
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candidate_title="$(jq -r '.title // empty' /tmp/gh-aw/data/candidate-raw.json)"
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sample_prompt="$(jq -r '.sample_prompt // empty' /tmp/gh-aw/data/candidate-raw.json)"
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evidence_summary="$(jq -r '.evidence_summary // empty' /tmp/gh-aw/data/candidate-raw.json)"
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if [ -z "$candidate_title" ]; then
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printf '%s\n' '{"type":"noop","message":"No candidate title was provided for drilldown."}' >> "$GH_AW_SAFE_OUTPUTS"
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exit 0
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fi
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slug="$(printf '%s' "$candidate_title" \
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| tr '[:upper:]' '[:lower:]' \
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| sed 's/[^a-z0-9]/-/g' \
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| sed 's/-\{2,\}/-/g' \
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| sed 's/^-//' \
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| sed 's/-$//' \
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| cut -c1-48)"
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if [ -z "$slug" ]; then
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slug="candidate"
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fi
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resolved_target_kind="$TARGET_KIND"
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if [ "$resolved_target_kind" = "" ] || [ "$resolved_target_kind" = "auto" ]; then
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case "$RECOMMENDATION_KIND" in
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continuous_workflow)
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resolved_target_kind="workflow"
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;;
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shared_prompt_or_chatops)
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resolved_target_kind="shared_prompt"
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;;
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keep_ad_hoc_but_standardize)
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resolved_target_kind="playbook"
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;;
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*)
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resolved_target_kind="workflow"
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;;
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esac
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fi
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sample_len=${#sample_prompt}
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evidence_len=${#evidence_summary}
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candidate_strength="weak"
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if [ "$sample_len" -ge 24 ] || [ "$evidence_len" -ge 40 ] || [ -n "$CANDIDATE_JSON" ]; then
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candidate_strength="strong"
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fi
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jq -n \
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--arg title "$candidate_title" \
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--arg slug "$slug" \
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--arg recommendation_kind "$RECOMMENDATION_KIND" \
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--arg resolved_target_kind "$resolved_target_kind" \
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--arg sample_prompt "$sample_prompt" \
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--arg evidence_summary "$evidence_summary" \
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--arg candidate_strength "$candidate_strength" \
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--slurpfile raw /tmp/gh-aw/data/candidate-raw.json '
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{
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title: $title,
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slug: $slug,
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recommendation_kind: $recommendation_kind,
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resolved_target_kind: $resolved_target_kind,
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candidate_strength: $candidate_strength,
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sample_prompt: (if $sample_prompt == "" then null else $sample_prompt end),
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evidence_summary: (if $evidence_summary == "" then null else $evidence_summary end),
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raw_candidate: $raw[0]
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}
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' > /tmp/gh-aw/data/candidate.json
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jq -n \
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--arg resolved_target_kind "$resolved_target_kind" \
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--arg slug "$slug" '
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{
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target_path: (
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if $resolved_target_kind == "workflow" then ".github/workflows/" + $slug + ".md"
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elif $resolved_target_kind == "shared_prompt" then ".github/workflows/shared/" + $slug + ".md"
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else ".github/workflows/shared/" + $slug + "-playbook.md"
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end
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),
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trigger_hint: (
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if $resolved_target_kind == "workflow" then "Prefer workflow_dispatch first; choose schedule or event triggers only when repeated evidence justifies automation."
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elif $resolved_target_kind == "shared_prompt" then "Prefer a reusable prompt or shared workflow component before full automation."
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else "Keep this human-in-the-loop and produce a reusable playbook or prompt template."
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end
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),
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implementation_bias: "Prefer the smallest durable artifact that reduces repeated prompting without widening scope.",
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report_style: "Use issue sections with visible summary and one fenced draft block."
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}
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' > /tmp/gh-aw/data/derived-plan.json
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---
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# Copilot Centralization Drilldown
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Turn one mined centralization candidate into a concrete, reviewable draft.
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Read these prepared files first:
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- `/tmp/gh-aw/data/candidate.json`
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- `/tmp/gh-aw/data/derived-plan.json`
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Stay narrow. Do not perform broad repo exploration. Use the current repository's existing workflow style as the default convention.
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If `candidate_strength` is `weak`, or the provided evidence is too vague to justify a concrete draft, call `noop` with a short explanation.
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## Task
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1. Decide the smallest useful deliverable:
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- a new gh-aw workflow draft
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- a reusable shared prompt/template draft
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- a reusable human playbook when automation is premature
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2. Produce exactly one primary draft aligned with `resolved_target_kind` unless the evidence clearly supports a better smaller artifact.
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3. Optimize for AI-credit savings by reducing repeated prompt construction, ambiguous setup work, or repeated context rebuilding.
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4. Keep the draft implementation-ready and short. Do not design a platform.
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## Output
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Use `create-issue`.
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Structure the issue with these `###` sections:
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- `### Summary`
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- `### Pattern Fit`
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- `### Proposed Draft`
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- `### AI Credit Savings Rationale`
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- `### Inputs Still Needed`
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- `### References`
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In `### Proposed Draft`:
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- include `Path: <target path>` on its own line before the draft
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- include exactly one fenced `md` code block containing the full proposed file content
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- if the best artifact is a workflow, produce a full gh-aw workflow markdown file with frontmatter and prompt body
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- if the best artifact is a shared prompt or playbook, produce the full markdown file content for that path
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## Constraints
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- Prefer `workflow_dispatch` for a first workflow draft unless the evidence strongly supports a scheduled or event-driven trigger.
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- Keep permissions read-only and route visible writes through safe outputs.
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- Reuse deterministic preprocessing or explicit `noop` guidance when that materially lowers token use.
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- Do not create more than one draft artifact in the issue.

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