SKILL.md
AI Token Improvement Plan Engineer
When to Use
- Audit where tokens are spent (by feature, model, tenant, turn type)
- Prioritize cost-saving initiatives with estimated savings and risk
- Draft a phased improvement plan with metrics and rollback criteria
- Define eval gates so cost cuts do not regress quality or safety
- Brief leadership on trade-offs (model tier, context size, agent depth)
When NOT to Use
- Implement context packing or compression code →
ai-context-engineer - Rewrite one prompt or agent tool schema →
prompt-engineer - Build RAG ingest, chunking, or agent runtime →
ai-engineer - Weekly cost review ritual and release governance →
ai-lead-ops - Memory store architecture →
ai-memory-developer - Adversarial safety testing →
ai-redteam - AI policy and regulatory mapping →
ai-risk-governance
Related skills
| Need | Skill |
|---|---|
| Context budget and truncation | ai-context-engineer |
| Prompt patterns and eval of wording | prompt-engineer |
| RAG and agent implementation | ai-engineer |
| Production ops and cost reviews | ai-lead-ops |
| Memory write/read policy | ai-memory-developer |
| Safety regression testing | ai-redteam |
| AI solution architecture (commercial/enterprise) | applied-ai-architect-commercial-enterprise |
| Token efficiency experiments and benchmarks | research-engineer-scientist-tokens |
Core Workflows
1. Scope and baseline
Define:
- Surface area — chat, copilot, batch, agents, embeddings-only
- Time window — 7/30 days; exclude anomalies (launches, incidents)
- Segmentation — model, feature flag, tenant tier, environment
Collect baseline metrics (see references/token_audit.md).
Deliverable: current state table with top 5 cost drivers (≥70% of spend if possible).
2. Categorize spend
Bucket each driver:
| Category | Examples |
|---|---|
| Input bloat | Long system prompts, duplicated docs, full chat history |
| Output bloat | Verbose defaults, no max_tokens, unconstrained agents |
| Model choice | Opus-class for simple classification |
| Retrieval | Over-fetching chunks, huge tool results in context |
| Agent loops | Extra tool rounds, retry storms |
| Infrastructure | Re-embedding unchanged corpora, log payloads in prompts |
Tag fixed per request vs scales with users/sessions.
3. Initiative backlog
For each idea record:
- Description and owner team
- Estimated savings (% tokens or $/month) with assumptions
- Effort (S/M/L) and dependencies
- Quality risk (low/med/high) and required evals
- Measurement — metric that proves success
Prioritize with impact × confidence ÷ effort; never ship without eval plan.
See references/improvementbacklog.md and references/optimizationplaybook.md.
4. Quality and safety guardrails
Every initiative must list:
- Golden-set evals (task accuracy, format)
- Safety set (if user-facing)
- Latency check (p95)
- Rollback trigger (e.g., CSAT drop, eval regression >X%)
See references/measurementandkpis.md.
5. Phased plan
Typical phases:
| Phase | Focus | Duration |
|---|---|---|
| 0 — Measure | Instrumentation, dashboards, attribution | 1–2 weeks |
| 1 — Quick wins | max_tokens, model routing, prompt trim, cache | 2–4 weeks |
| 2 — Structure | RAG top-k, history compression, tool output limits | 4–8 weeks |
| 3 — Architecture | Agent budget caps, routing policies, batch/offline | 8+ weeks |
Each phase: goals, initiatives, owners, exit criteria.
See references/rollout_plan.md.
6. Executive summary
## Situation — spend and growth rate
## Target — $ or tokens/session goal by date
## Top drivers — ranked list
## Plan — 3–5 initiatives per phase
## Risks — quality, latency, eng capacity
## Asks — budget, headcount, eval timeHand ongoing ops to ai-lead-ops after plan approval.
When to load references
- Baseline and attribution →
references/token_audit.md - Backlog template and scoring →
references/improvement_backlog.md - Technique catalog →
references/optimization_playbook.md - KPIs and eval gates →
references/measurementandkpis.md - Phasing and rollback →
references/rollout_plan.md