--- name: LLM Campaign Drift Gate slug: llm-campaign-drift-gate category: Quality description: Verifies LLM model consistency before resuming a paused batch-scoring campaign by comparing fresh responses to cached outputs. Detects silent model drift from alias repoints or serving revisions to prevent splicing different model versions into one measure. github: "https://github.com/kennethkhoocy/applied-micro-skills/tree/main/plugins/applied-micro/skills/llm-campaign-drift-gate" language: Python stars: 48 forks: 0 install: "npx degit https://github.com/kennethkhoocy/applied-micro-skills/tree/main/plugins/applied-micro/skills/llm-campaign-drift-gate ~/.claude/skills/llm-campaign-drift-gate" installs_to: ~/.claude/skills/llm-campaign-drift-gate source_path: plugins/applied-micro/skills/llm-campaign-drift-gate/SKILL.md collection_size: 25 category_size: 1354 collection_url: "https://dirskills.com/collections/kennethkhoocy/applied-micro-skills" added: 2026-08-11T07:21:21.387Z last_synced: 2026-08-11T07:21:21.387Z canonical_url: "https://dirskills.com/skills/llm-campaign-drift-gate" --- # LLM Campaign Drift Gate Verifies LLM model consistency before resuming a paused batch-scoring campaign by comparing fresh responses to cached outputs. Detects silent model drift from alias repoints or serving revisions to prevent splicing different model versions into one measure. **Install:** ```bash npx degit https://github.com/kennethkhoocy/applied-micro-skills/tree/main/plugins/applied-micro/skills/llm-campaign-drift-gate ~/.claude/skills/llm-campaign-drift-gate ``` ## README # LLM Campaign Drift Gate ## Problem Batch-scoring campaigns (exposure measures, classifiers, extraction runs) call provider aliases that can be silently repointed to a new model at any time. Resuming a half-finished campaign after the alias moves splices two different scorers into one variable, with the version boundary correlated with whatever orders the chunks (time, firm id) — a silent confound. Providers can also RETIRE the old model entirely, making the original campaign uncompletable. ## Context / Trigger Conditions - Resuming a scoring run more than ~a day after its last paid chunk - "Top up credits and finish the run" requests - Any incremental scoring against an existing response cache - Symptom of a missed gate: a step-change in scores at a resume boundary ## Solution Before ANY production spend on resume, run a two-part gate (~$0.30–2): 1. **Canary (the decisive check):** sample ~100 already-cached items, re-send their EXACT stored prompts fresh, compare fresh vs cached scores. Gate: ≥97% all-field exact match and no systematic directional shift. Write the comparison in a standalone script — never through the pipeline's cache layer, which would overwrite production entries. 2. **Gold re-validation:** re-score the gold/validation panel fresh and compare agreement metrics to the prior validation (e.g. median F1/κ within ~0.03, no domain dropping >0.10). Also capture `response.model` on every gate call — pipelines rarely store it, and it is the only direct evidence of a repoint. Check the provider's `/models` endpoint: if the old model id is gone, no rollback exists. 3. **If the canary fails, diagnose BEFORE concluding — two mandatory follow-ups:** - **Date the suspected flip against the provider's changelog** before inferring a model splice. `response.model` on fresh calls identifies today's model only; if the alias already pointed there when the cache was written, there is no family splice and the mismatch needs another explanation. (Verified failure mode: an alias that had served the "new" model for months was misread as a fresh repoint.) - **Fresh-vs-fresh canary** to separate serving drift from temperature-0 nondeterminism: re-score the same items a second time. Drift signature = fresh2-vs-fresh1 agreement high and symmetric while both fresh runs disagree with the cache at a higher rate in the SAME signed direction. Noise signature = fresh-vs-fresh disagrees about as much as fresh-vs-cache, with no directional bias. - Supporting forensic: compare raw-response formatting fingerprints (JSON pretty/compact ratio, key order) between cache and fresh — a heterogeneous or shifted style distribution corroborates a serving change when no model id was recorded. **Key subtlety (why both checks):** a new model or revision can validate AGAINST GOLD as well as the old one (κ holds or improves) while still disagreeing with the old scores on 10–30% of items, concentrated in borderline-heavy fields. Gold agreement does not license splicing — the gate fails on the canary alone. And alias stability is not serving stability: the same alias serving the same model family can still drift across days via silent serving revisions; a canary-failed resume is a seam either way, and the decision (resume with a documented seam vs re-score the universe) belongs to the budget owner. ## Verification The gate script logs: fresh `response.model` ids, canary exact-match rate, per-field mismatch counts with signed direction, and the gold-metric deltas. GO only if both checks pass. ## Example T1 exposure_v2 resume, 2026-07-16: canary returned 71% exact (gate ≥97%) with a litigation-concentrated negative shift, yet holdout median κ improved 0.607→0.644. First interpretation — "alias repointed to a new model family" — was WRONG: the provider changelog showed `deepseek-chat` had served v4-flash since April, months before the campaign. The fresh-vs-fresh follow-up then isolated the true cause: fresh2-vs-fresh1 93% exact/symmetric/litigation 0, both fresh runs vs cache 71–72% with litigation −12 identically — a serving revision within the same model across a 2-day gap, corroborated by a shifted JSON-formatting fingerprint. Total diagnosis cost ~$0.30; the resume-vs-rescore decision went to the budget owner with the seam quantified. ## Notes - Design campaigns for this failure: per-response content-addressed cache + append-only checkpoint makes "re-score everything under the new model" a clean cache-rotation, not a data loss. - If the cache key embeds the alias string rather than the resolved model, record actual `response.model` in run reports — the cache cannot tell you later which model produced an entry. - One campaign = one model. Budget and schedule so the universe completes within days, or accept that a provider release can force a full re-score. - See also: [llm-gold-bound-failure-check] for the companion pre-campaign check — whether a validation-gate failure is fixable by prompt at all, or bound to the gold construct.