lower-anthropic-bill

Lower Anthropic Bill

Use this worker for the installer happy path and for any developer who arrives with a Claude bill, Anthropic usage export, or codebase that calls the Messages API. The first deliverable is a local audit: call-site inventory, current price assumptions, tokenizer re-baseline risk, cache-hit opportunities, batchability, and route candidates. Do not edit code during the audit.

Safety Gates

• Local-first. Static repo inspection and local usage-export parsing are the default. Do not upload source, prompts, traces, completions, datasets, repo paths, secrets, or private notes without explicit approval for that exact action.
• No provider spend without a named surface, model, data class, row count, and dollar envelope. Token-counting, cache probes, OpenAI migration tests, and GEPA reflection all need approval if they call a provider. Recommend the decision-sized test and explain its expected savings, time, and confidence; one approval may cover the named bounded test matrix.
• No silent source edits. Adding cache_control, changing model strings, rewriting prompts, or adding an OpenAI route is a follow-up change after the audit report is reviewed.
• Treat dollar values as estimates until backed by usage exports or measured runs. Savings claims require the normal claim.json evidence path from ../optimize-workload/SKILL.md.

Intake

Default to the current repo. Ask for at most one missing input:

• a path to the app or service that calls Anthropic;
• an Anthropic usage export, gateway capture directory, or sampled response usage block;
• monthly call volume per route if no export exists.

If the installer prompt set the lower-Anthropic-bill goal, assume the objective is cost reduction with no quality regression. Ask only for the target repo or usage export if you cannot infer it.

If the developer asks to inspect billing email, invoices, receipts, or an authenticated billing website, route that optional evidence source through ../inspect-billing-sources/SKILL.md before estimating hotspots from bill data.

Flow

1. Refresh vendor facts. Read reference.md before quoting prices, tokenizer changes, cache minimums, batch discounts, or OpenAI migration advice. Re-verify online when the work will be sent externally.
2. Inventory Anthropic call sites. Inspect dependencies, wrappers, env var names, model IDs, prompt builders, tool definitions, retries, batch jobs, and tracing. Use the scan checklist in reference.md and surface the inventory before recommending changes.
3. Add optional bill evidence. If the developer approved email, invoice, usage-export, or browser inspection, read the hotspot ledger produced by inspect-billing-sources and join it to the call-site inventory. Keep unattributed spend explicit.
4. Re-baseline token risk. Flag Opus 4.7+ or newer model upgrades, because Anthropic documents a new tokenizer that can increase token counts for the same text. Prefer usage blocks or /v1/messages/count_tokens on synthetic or approved payloads; otherwise report this as a risk requiring measurement.
5. Audit prompt-cache structure. Check whether stable tools, system prompts, few-shots, schemas, documents, and long histories are eligible for caching; whether volatile values appear before cache breakpoints; whether prefixes meet current model minimums; and whether response usage shows cache_read_input_tokens.
6. Rank the opportunity ledger. Group findings by route and estimate addressable spend only from explicit volume, usage exports, or clearly labeled synthetic assumptions. Rank by addressable spend × confidence × expected implementation leverage so a small, easy tail item does not displace the concentrated cost center. Include confidence: high, medium, unknown, or pending eval. When value depends on who or what consumes an output now or later, do not invent a universal taxonomy: expose the candidate segments and let the developer drill into representative rows before labeling work as low-value or removable.
7. Pick candidate interventions. Start with the intervention offering the largest expected addressable savings at sufficient quality and confidence. Consider cache fixes, batch moves, max-token/output tightening, older or cheaper Anthropic models, local/open-weight candidates, OpenAI routes, and GEPA prompt repair in the order supported by the opportunity ledger—not a fixed cheapest-first ladder. Prefer the lower-cost test only when alternatives have comparable expected information value. Follow ../understudy/reference.md → Outcome-first spend posture. Route model comparisons to ../compare-model-sweep/SKILL.md and GEPA to ../optimize-workload/SKILL.md.
8. Stop at the audit unless asked to implement. If the developer chooses a fix, make one small reviewable change and verify it with the relevant usage field or eval. Do not bundle unrelated route migrations.
9. Offer anonymous savings sharing only after evidence exists. When a value report or claim.json supports the result, route to ../share-savings/SKILL.md. Never send prompts, traces, repo names, company names, or contact details.

Output Standard

End with:

• repo or export inspected;
• Anthropic call-site inventory;
• tokenizer re-baseline status;
• cache-hit findings and exact invalidators, when known;
• opportunity ledger with assumptions and confidence labels;
• route candidates split into cache, batch, cheaper Anthropic, OpenAI, and local/open-weight lanes;
• result type: audit, measured sample, migration plan, validation, or blocked;
• one recommended next local command or approval-gated test.

References

• reference.md — sourced pricing/tokenizer/cache facts, call-site scan checklist, cost math, OpenAI migration lane, and GEPA gates.
• ../ingest-traces/references/profile-captures.md — parse a whole capture directory into spend by model and call type.
• ../optimize-workload/references/prompt-cache-optimization.md — detailed prompt-cache debugging and parity rules.