Prompt repair that doesn’t wait for someone to remember

The use case

Your LLM app already looks "done" from a git perspective: prompts live in the
repo, gold labels live in JSONL, and a harness command can score quality offline.
You can migrate a model or refine after a label change when someone remembers
to.

In practice, nobody owns that loop. Deprecations arrive as a Slack forward of a
provider email. Someone bumps the model ID under deadline pressure (see
post 1). Separately, annotators merge
label updates and CI never re-tunes the prompt (see
post 2). Cost opportunities sit
unnoticed because no one runs a weekly triage. Each incident invents a one-off
runbook; the next person cannot see the same decision table.

What you want is Dependabot's shape applied to prompts: something that
watches model lifecycle and eval data, tests candidates through your
real harness, and opens a PR or issue with evidence — without requiring a
hero engineer every Monday.

What driftless does here: scaffold GitHub Actions (init-ci), apply a policy
file for when to propose changes, and run scan / plan / migrate / refine
through a composite Action so triage is scheduled and reviewable.

Artifact reference:
EXAMPLE_SUCCESS_PR.md distinguishes public
testbed PR #4 from the different bundled four-row saved success fixture. Both
show the open-pr evidence shape, but the published CLI does not ship the
testbed-specific deterministic patch tooling used to prepare PR #4.

Open the public draft PR and inspect the generated report and diff.

This post maps each layer to the
support-classifier-svc
workflows you can run today.

The testbed at a glance

Two workflows in one driftless.yml:

Policy in .driftless/policy.yml (committed in the testbed):

deprecation:
  enabled: true
  warn_before_days: 90
  action: pr

cost:
  enabled: true
  min_savings_pct: 0.20
  max_quality_drop: 0.01

data_change:
  enabled: true
  min_changed_rows: 5

Both workflows still sit on deprecated gpt-3.5-turbo, so plan always
has work — useful for demoing CI.

The shape is intentionally Dependabot-like:

Scaffold CI from your contract

Greenfield repos:

driftless init-policy    # .driftless/policy.yml
driftless init-ci        # .github/workflows/driftless-*.yml

The testbed instead dogfoods hand-written workflows pinned to
driftless==0.3.0 — copy patterns from
.github/workflows/.

Layer 1: Weekly plan (simulator, free)

plan-preview.yml
runs every Monday:

env:
  SUPPORT_CLASSIFIER_SIMULATE: "1"

steps:
  - run: driftless plan || test $? -eq 1
    # exit 1 = triggers found (expected here)

Local equivalent:

cd support-classifier-svc
export SUPPORT_CLASSIFIER_SIMULATE=1
driftless plan

Actual output (July 2026):

┃ Workflow          ┃ Trigger     ┃ Migrate                    ┃ Naive     ┃ Decision      ┃
│ support_classifier│ deprecation │ gpt-3.5-turbo -> gpt-4o-mini │ regresses │ ISSUE (critical) │
│ quick_triage      │ deprecation │ gpt-3.5-turbo -> gpt-4o-mini │ regresses │ ISSUE (critical) │

Why:
  gpt-3.5-turbo retired 277d ago; candidate gpt-4o-mini not shippable as-is
  (status=blocked) -> open issue

2 workflow(s) need action across 1 model move(s):
  gpt-3.5-turbo -> gpt-4o-mini (deprecation): support_classifier, quick_triage

One grouped move, two workflows — not two blind migrate PRs. Artifacts land under
.driftless/reports/ for the run.

Why ISSUE not PR? Naive compare fails thresholds (100% schema errors on
the simulator — see post 1). Policy
still surfaces the row; migrate + repair is required before a PR is safe.

Read the table as a routing layer, not an approval:

Layer 2: plan --act when you're ready to close the loop

plan-act.yml
— manual dispatch, optional --create:

driftless plan --act              # preview git/gh operations
driftless plan --act --create     # run migrate/refine + open PRs/issues

Scheduled runs stay dry-run; flipping create=true on dispatch actually opens
PRs. Needs OPENAI_API_KEY (and GH_TOKEN for GitHub).

Keep scheduled plan --act dry until the issue/PR distinction matches your
team's risk tolerance. The first few runs are calibration: you are teaching the
policy which changes deserve automation and which deserve a human migration
ticket.

Layer 3: Event-driven refine (dataset drift)

When someone commits label changes →
refine-on-label-change.yml:

push to evals/tickets.labels.jsonl
  → audit-labels --fail
  → refine --strict-label-audit  (simulator harness)
  → open-pr --create

See post 2 for the charge-reversal
policy story (evals/_apply_refund_policy.py, 25 tickets).

Layer 4: Manual migration (real API, full PR)

migrate-on-model-change.yml
— Actions → Migrate model → Run workflow:

Steps: audit-labels → real compare → migrate --strict-label-audit →
open-pr --create. Requires OPENAI_API_KEY. Timeout 120 minutes on 290
tickets with LLM repair.

This is the PR you show stakeholders: scorecard, diffs on prompts/system.md
and config/llm.yml, holdout line, attempt log.

Layer 5: Label hygiene on every eval PR

audit-labels.yml
on PRs touching evals/tickets.*.jsonl — blocks merge if similar inputs disagree
on gold labels.

Suggested rollout order

1. audit-labels on eval file paths (cheap, catches bad data early)
2. plan-preview weekly with simulator (visibility, no keys)
3. refine-on-label-change when labels live in git
4. migrate-on-model-change manual dispatch when you accept token cost
5. plan-act --create only after policy thresholds feel right

That order keeps the blast radius small. First make the eval trustworthy, then
make drift visible, then let automation propose changes.

Workflow cheat sheet (testbed)

Next steps

• Post 1: compare + migrate
• Post 2: refine on label change
• Post 4: cost trigger — needs an
  active baseline model; testbed deprecation rows dominate until you migrate
• Product workflows: driftless .github/workflows/