understudy

name	understudy
description	Use when a developer asks a coding agent to improve an LLM app or agent — "make my LLM app cheaper/faster", "raise quality or reliability", "compare models", "pick a model or route". Orchestrates trace → evaluate → optimize (GEPA, automatic prompt evolution) → compare → deploy via worker skills. Not for generic coding unless LLM behavior, cost, traces, evals, or routing is involved.
metadata	{"understudy":{"mode":"automatic","safety":"approval-required","cli_required":false}}

Understudy

Understudy is agent improvement infrastructure: it helps a coding agent improve its developer's LLM system from real traces. This skill is the orchestrator — it gives your agent the loop and routes each stage to exactly one worker skill. It does not do the work inline.

The loop is backend-agnostic: it can begin locally without registration, but the default route is the strongest active model and managed cloud execution when the developer has not selected Local. A dropped dataset authorizes analysis through the active model. Activating a named, bounded cloud workflow authorizes its normal uploads, provider calls, temporary resources, evaluation, receipts, and cleanup.

The improvement loop

1. Understand the codebase — find where LLM calls happen and the current model / provider / harness / routing / eval setup.
2. Understand the objective (cost, speed, quality, reliability, compliance, or a weighted mix).
3. Understand the constraints (what must not be violated).
4. Understand the workload — inspect prompts in situ, trace the request/response code path, profile the dataset/traces, and confirm the task meaning with the user.
5. Capture or locate real traces.
6. Build or improve a decision-sized, representative eval harness; rerun the incumbent baseline.
7. Run local optimization against eval failures.
8. Compare candidate vs baseline on the objective.
9. Recommend the best route for the stated objective — harness, model, supplier, gateway/inference-layer route, deployment approach — with cost and time transparent.
10. Implement the selected route safely with the smallest coherent change that fully addresses the measured cause.
11. Produce an Understudy Agent Improvement Report the developer can review.

Frame every job

Keep these six separate and explicit — say them back before acting:

• Objective — what are we optimizing for?
• Constraints — what are we not allowed to violate?
• Evidence — what traces / evals / prices / measurements do we have?
• Workload — what task does the prompt/data/code path actually represent?
• Route — what harness / model / supplier / deployment path?
• Action — what does the agent actually change?
• Verification — how do we prove the change helped?

Default mode

• Use the strongest active route; prefer managed cloud unless the developer selects Local or a hard constraint requires it.
• Inspect before changing.
• Measure before optimizing.
• Optimize before routing.
• Compare before deploying.
• Recommend an outcome-sized plan with cost, time, data scope, and expected evidence. Treat the developer's launch action as authorization for that displayed envelope.

Show partial findings early ("found 3 LLM call sites"; "app uses LiteLLM, so gateway insertion is low-risk"; "no evals — I'll build a decision-sized harness"; "stated ZDR constraint blocks hosted upload unless approved").

Engagement & pacing

Keep the user oriented and engaged across the loop's slow steps. Full doctrine in ../../docs/engagement-and-pacing.md; the rules:

• Estimate before you start. Say how long a step takes and what it costs before running it (e.g. "100-task baseline ≈ 15–20 min, ~$31"). Measure a small sample first if you don't know the rate.
• Background the slow thing — first. Downloads, baselines, and sweeps run in the background, started before the interactive part so they finish during it.
• Fill the wait. While a long run goes, cost-model the candidate models at the user's real volume and pull their benchmarks so the comparison is ready the moment scores land.
• Plan up front; show where they are. Open with a concrete plan; each turn, state the loop stage (capture → baseline → optimize/compare → validate → decide), re-derived from artifacts on disk.
• Meet them where they are. Read ~/.understudy/profile.json and set vocabulary, coaching depth, and opinion strength to match. Also read ~/.understudy/agent-card.json when the user asks whether their Understudy is active or how to talk to it; this card is the agent-facing runtime source of truth for model endpoint, tmux session, companion status, and the exact local chat command. No profile yet → run ../onboard/SKILL.md first.

Inspect before you ask

Answer from the repo before asking the developer. Read package files, provider/ model wrappers, env vars, LLM SDK usage, agent frameworks, prompt files, eval and test dirs, tracing/logging, deploy and CI config, and any compliance/README/ architecture docs. The repo-inspection and call-site checklist lives in ../capture-evidence/SKILL.md and its reference.md.

Ask only what you genuinely cannot infer:

1. What are we optimizing for? (cost / speed / quality / reliability / compliance / weighted mix)
2. What is the target task? (e.g. support triage, tool-use, classification, extraction, retrieval QA)
3. Which constraints matter? (SOC 2, ZDR, approved providers, no data leaves the machine, max cost/latency, required model family, region)
4. Which environment? (local-only, Understudy hosted, private gateway, hybrid)

See reference.md for the intake, objective menus, constraints, route-selection taxonomy, fresh-pricing rule, the report template, anti-patterns, and worked examples.

Route to one worker

Identify the developer's current stage and load exactly one:

• Anthropic / Claude bill reduction — the developer says "lower my Anthropic bill", "audit my Claude spend", "why is cache_read zero", asks to find Anthropic calls, or arrives through the --lower-my-ant-bill installer path → ../lower-anthropic-bill/SKILL.md first. It audits call sites, tokenizer risk, cache structure, batchability, and route candidates before any code edits or provider calls.
• Billing email / browser source inspection — the developer wants to inspect Anthropic billing emails, receipts, invoices, usage exports, or an authenticated billing website for spend hotspots → ../inspect-billing-sources/SKILL.md. This is optional evidence for lower-anthropic-bill and requires exact read scope approval before email or browser access.
• First run / new user / no profile yet — the developer is new, just installed the plugin, or asks "where do I start?" and ~/.understudy/profile.json is missing → ../onboard/SKILL.md (backgrounds a small model, profiles the machine, interviews, writes the profile).
• Product explanation / company context / onboarding copy — the developer asks what Understudy is, who Aamir Poonawalla and Luis Manrique are, how the company began, or how Desktop, local serving, candidate results, rollout lab, or training workflows work → ../product-knowledge/SKILL.md.
• Workload unclear — the repo has prompts, traces, datasets, eval rows, or LLM call sites, but the task, data shape, request/response path, or success criteria have not been explained and confirmed → ../understand-workload/SKILL.md. Do this before head-to-head comparisons or optimization.
• Codebase / evidence not yet pinned down — LLM call sites, current model/ harness, traces, metric, splits, or incumbent baseline are missing, ambiguous, or stale → ../capture-evidence/SKILL.md (also owns repo inspection + eval-harness discovery/build).
• App is running but no traces exist yet — the developer wants capture flowing in minutes with no app-code changes ("instrument my app", "start capturing my LLM calls") → ../instrument/SKILL.md (env-var redirect through the gateway, verified test call, then hands off to ingest-traces).
• Traces already exist — a bucket of captures, a provider log export, or gateway capture files are in hand and the developer wants them turned into local eval sets, profiled for where the spend goes, or triaged in bulk by content ("which runs failed", "label these by failure mode") → ../ingest-traces/SKILL.md and its fleet lenses (references/profile-captures.md for structure/cost, references/lotus-semantic-triage.md for content-level bulk triage). When the source is Desktop supervisor interventions, first run understudy desktop supervision export --reviewed-only --json; use its content-addressed correction-pair JSONL as the local input and inspect the accompanying attribution metrics. Unreviewed pairs may support error analysis, but are not human-approved training labels.
• Inference / routing / capture / auth / deploy — Understudy inference, gateway trace capture, project/key management, model A/B via route traffic %, understudy run, or registering an improved route → ../use-understudy-gateway/SKILL.md.
• Frontier access choice — a local-vs-frontier comparison, installer, or agent run needs to decide between the Understudy managed catalog, BYO .env provider keys, or skipping remote frontier calls → ../use-understudy-gateway/SKILL.md and its frontier-keys lens (references/frontier-keys.md).
• Pre-launch change gate — a model-level change (model swap, route change, prompt or playbook edit) is about to ship and the developer wants an offline verdict first: "will this work in prod", "run a simulator before I flip traffic", "gate model changes like a pre-commit check" → ../simulate-before-launch/SKILL.md.
• Production ramp / rollback — a route decision exists and live traffic must move safely: "ramp this route", "move 25% of traffic to the new model", "did the route change regress anything", "roll this back", "prove the savings are real" → ../ramp-and-verify/SKILL.md.
• Routing diagnostics / provider health — the developer asks "is Understudy causing my errors", "which workloads are routed", "check provider health", "are there 500s", "what's our error rate", or wants self-service diagnostics without asking the team → ../check-routing-health/SKILL.md.
• Share savings / leaderboard receipt — the developer wants to share how much money they saved, submit an anonymous lower-Anthropic-bill result, or send metrics back for the coming leaderboard → ../share-savings/SKILL.md. Require the metrics-only payload review and explicit approval before posting.
• Acquire / cache / organize local models — download a model, see what's already cached, free up model disk, pick which Gemma/Nemotron to pull, or explain how open weights work → ../manage-local-models/SKILL.md.
• Local model experiment / local-only route — evaluate a local or workstation-hosted model through the same frozen workload/eval, choose a runtime, compare local vs remote, or satisfy ZDR / no-upload constraints → ../run-local-model-lab/SKILL.md.
• Benchmark-lab lifecycle / run queue / rigor — the developer has (or wants) a trace-compiled benchmark directory and asks to operate it end to end: review tasks, apply the auto-accept policy, calibrate with the incumbent and null/spam trivial floors, queue candidate or prompt-override runs, keep the run executor (understudy runs execute --watch) alive, read rigor-report.md, or run an app_replay regression after code edits → ../operate-benchmark-lab/SKILL.md (MCP tools preferred; CLI verbs otherwise). For only the app-replay slice, ../replay-app-harness/SKILL.md.
• Multi-model sweep / Pareto comparison — the developer has a frozen eval and wants to run the same rows across several local, gateway, or frontier models to compare quality, latency, cost, and reliability → ../compare-model-sweep/SKILL.md. When the sweep verdict supports changing production traffic, don't stop at the report — the answer to "how do I get this running in prod?" is ../ramp-and-verify/SKILL.md (add provider → set route → staged ramp gated by the production validator).
• Token-level logprob comparison — the developer has two same-family eval runs with stored logprob sidecars and wants to see the exact moment a smaller, quantized, or weaker model diverged from a larger/full-precision sibling on the same row → ../compare-trajectories/SKILL.md and its token-logprob lens (references/logprob-lens.md).
• Tool-trace failure diagnosis — an environment-backed or tool-calling run failed and the developer asks whether the model lacked evidence, failed to retrieve visible evidence, trusted the wrong authority, acted too early, missed exact format, failed ID resolution, or exposed a scorer/harness issue → ../understand-workload/SKILL.md and its tool-trace forensics lens (references/tool-trace-forensics.md).
• Pedagogical learning / privileged-context self-teaching — the developer has answer keys, execution feedback, verifier traces, oracle tool labels, or canonical solutions and wants the local model to learn from trajectories that are both correct and learnable before SFT, GRPO, or hosted RL → ../local-distillation-lab/SKILL.md and its pedagogical arm (references/pedagogical-arm.md).
• RLM pedagogical training / verifiers / Prime RL — the developer wants to train or evaluate a Recursive Language Model policy over stateful trajectories, use privileged context as training/scoring signal, measure on-policy state coverage or surprise concentration, or prepare a Prime Intellect verifiers / prime-rl path → ../recursive-language-model/SKILL.md and its pedagogical-training lens (references/pedagogical-training.md).
• Hosted run planning — the developer wants to run a hosted job (fine-tune, RL run, big eval) and asks where to run it, how long it takes, or what it costs → ../plan-hosted-run/SKILL.md.
• Single-output optimization — fresh artifacts exist and the developer wants to validate, optimize (GEPA), compare candidates, or claim readiness → ../optimize-workload/SKILL.md.
• Agentic / tool-use optimization — multi-turn agent that calls tools, from read-only search loops to multi-step REST/API workflows that mutate state and must pass final-state validators; evaluate, A/B-compare models, or optimize its prompt/route → ../optimize-agentic-workload/SKILL.md (verifiers env or resettable sandbox, model A/B through the gateway, prompt-GEPA for the cheap model).
• RL / stateful-policy training handoff — local RLM/distillation rungs are insufficient and the agent must learn multi-step behavior in external or hosted training → ../prepare-verifier-handoff/SKILL.md (decide → author env → package → hand off).

Multi-turn or tool-use alone is NOT a handoff: agentic evaluation, A/B, and prompt/route optimization stay local in optimize-agentic-workload. RLM policy training routes to recursive-language-model (pedagogical training) first; only external/hosted RL handoffs route to prepare-verifier-handoff. When in doubt, route to capture-evidence — optimizing without a current harness/metric/split/baseline creates false progress.

Local specialization sequencing

When the goal is "train an understudy" / "can a local model do this?", the orchestrator owns the ladder — sequence workers, don't reimplement them:

1. Best-fit rung first. Pick the local model most likely to answer the task question within the available hardware and time (easy classification/ extraction → Gemma 4 E2B first; coding/structured generation → E2B feel-test then E4B/12B; tool-use → E2B only if the tool surface can be bounded; long-context/high-recall → start with a stronger local or remote anchor). Never start at the smallest model blindly, and never evaluate *-assistant drafter checkpoints as standalone candidates. Use ../manage-local-models/SKILL.md and ../run-local-model-lab/SKILL.md.
2. Optional frontier head-to-head for calibration — use ../ladder/SKILL.md when the developer needs a no-data local-vs-frontier feel test. Prefer the Understudy managed catalog when the frontier id is listed, so the comparison uses one gateway key and no provider key handling. Preference is a gap signal, never a claim.
3. Freeze the eval (capture-evidence; for workflow/tool tasks, design-simulated-environment), with frontier as the incumbent baseline and local as the candidate. Then write a gap report and let the gap pick the rung:
   • model too weak, harness sane → climb the Gemma/Nemotron ladder;
   • prompt or output contract weak → GEPA / prompt repair (optimize-workload);
   • workflow/tool state matters → seeded env (design-simulated-environment);
   • small model drowns in one giant prompt → RLM decomposition (recursive-language-model);
   • local handles easy cases only → hybrid / local-as-router (run-local-model-lab + use-understudy-gateway);
   • local cannot meet quality → stay remote and record the revisit trigger (new runtime, new weights, larger hardware, better env feedback).
4. Rerun until the claim is real: local beats the incumbent on the agreed metric for the frozen slice, or the route stays remote — measured, not vibes.

Safety Gates

Default to the path with the highest expected progress toward the stated objective under the developer's hard constraints. Make spend, wall-clock, data scope, expected evidence, and alternatives visible; do not silently choose the weakest model, smallest cohort, or narrowest intervention because it is cheap. Follow the outcome-first spend posture in reference.md. The developer's direct request or activation of a named, bounded workflow is the confirmation for its ordinary external writes, uploads, provider calls, spend, temporary serving, evaluation, receipts, and cleanup. Do not add dry-run or phase-by-phase approval gates. Ask again only before changing credentials, writing to production business systems, or expanding the displayed data, destination, spend, retention, or production-impact envelope.

Do not ask the developer to register, authenticate, paste secrets, or configure provider keys before the local evidence loop has found a concrete need. When that need exists, route through understudy login --email rather than a pasted key.

Follow the public boundary in ../../docs/privacy-and-data-boundaries.md: workload content may move through the active model and the provider named by an activated workflow. Never print, commit, or transmit secrets, and honor hard constraints the developer explicitly selected (SOC 2, ZDR, Local, approved providers) — see reference.md.

Never claim a cost, latency, quality, or availability win without measured before/after evidence, and never make pricing/availability claims from memory — use fresh data and label assumptions (reference.md).

MVP Artifact Contract

.understudy/capture-evidence/harness.json
.understudy/capture-evidence/environment.json
.understudy/capture-evidence/metric.json
.understudy/capture-evidence/splits.json
.understudy/capture-evidence/baseline.json
.understudy/experiments/<exp-id>/experiment.json
.understudy/experiments/<exp-id>/candidate.json
.understudy/experiments/<exp-id>/claim.json
.understudy/experiments/active            # pointer to the active <exp-id>

capture-evidence creates or refreshes the first five — the workload evidence every experiment runs against. Freshness is hash-bound: baseline.json carries harness_sha256, metric_sha256, and splits_sha256; any later change to the harness, metric, validator, or splits routes back to capture-evidence.

Experiment

An experiment is one measured episode over a frozen workload: an incumbent baseline versus a candidate, under a pinned metric and split, producing a claim and an outcome. It is the unit optimize-workload writes and the unit that graduates into the knowledge base. Each experiment owns a directory under .understudy/experiments/<exp-id>/; experiments/active names the one in flight, so a workload can hold more than one without overwriting prior results.

experiment.json is the record of that episode:

{
  "experiment_id": "exp-001",
  "workload": "workload-001",          // real id locally; anonymized only on promotion
  "objective": "cost",                 // cost | speed | quality | reliability | compliance | weighted
  "hypothesis": "Gemma-4-31b replaces the incumbent for triage tool-calls under break-even",
  "pins": {                            // copied from baseline.json's hash-chain
    "harness_sha256": "…",
    "metric_sha256": "…",
    "splits_sha256": "…"
  },
  "incumbent_model": "openai/gpt-4o",
  "candidate_model": "google/gemma-4-31b-it",
  "result": { "claim_ref": "claim.json", "quality_delta": null,
              "p50_latency_delta_ms": null, "cost_per_1k_delta_usd": null },
  "outcome": null,                     // success | partial | abandoned
  "route_decision": null               // ship-local | local-as-router | hybrid | remote
}

The record stores identity + input-pins + verdict — never a loop-step counter. Where you are in the loop is derived: re-hash the artifacts on disk and compare to pins (a changed hash means re-baseline); a present candidate.json with a null outcome means you are at compare. The filesystem and the pins are the truth, so the record cannot drift into reporting a step it isn't at.

outcome, workload, incumbent_model, candidate_model, and the result deltas mirror the type: experiment lab-note in the Understudy knowledge base — experiment.json is its un-anonymized, hash-pinned, live precursor. When outcome is set, understudy experiments promote writes a lab-note draft locally from the record. It does not anonymize or upload: the draft carries the real local values behind a review banner, and scrubbing workload to workload-NNN, dropping real paths, and filling the cost/margin fields the local record omits are part of the publish review. Publishing to a shared repository is an upload — gate it like any other (see Safety Gates).

understudy experiments new opens an experiment and copies the baseline hash-chain into pins; understudy next reads the artifacts on disk plus the active experiment to report the loop step and the command to run next; understudy experiments outcome records the verdict. On optimize-workload adapter run --execute the candidate is frozen into the active experiment directory automatically; understudy experiments freeze does it explicitly (and freezes a --claim-from <path>). The optimizer's working files stay under .understudy/optimize-workload/.

Removed Python-prototype commands and deleted draft skills are tracked in ../../docs/current-functionality.md. Do not route to deleted skills or commands.

Output Standard

End with:

• worker skill used or recommended;
• where you are in the loop, and the Objective / Constraints / Evidence / Route / Action / Verification state, plus the Workload understanding state;
• artifacts inspected, created, or still missing;
• result type: evidence-capture, validation, optimization, route-recommendation, deployment, or blocked;
• activated workflow envelope and any expansion that would require a new decision;
• one recommended next command or local action.

For a completed improvement, produce the Understudy Agent Improvement Report (template in reference.md).