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name: cohere-cost-tuning description: >- Model and reduce Cohere usage cost with current pricing, measured token or search units, quality gates, caching, and budget controls. Use when forecasting or optimizing Cohere spend. Trigger with "Cohere cost", "Cohere pricing", or "Cohere budget". argument-hint: "[workload] [monthly-volume] [currency]" allowed-tools: Read, Glob, Grep, WebFetch, Write, Edit version: 1.6.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT tags:

• saas
• cohere
• cost model: inherit effort: high compatibility: Designed for Claude Code; live verification requires network access and an approved Cohere API key

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Cohere Cost and Usage Tuning

Overview

Build a reproducible cost model from live prices and measured workload units, then optimize only within approved quality and safety thresholds.

Prerequisites

• The target repository, runtime, environment, and accountable owner
• An approved Cohere team and key for any live verification
• Current quality, security, privacy, capacity, and change-control requirements

Tool Discipline

Use Read, Glob, and Grep to inspect code, configuration, and evidence. Use WebFetch only for current Cohere primary documentation. Use Write or Edit only when the user requested implementation and the exact target files are known; never write credentials or customer content.

Current Contract

• Do not embed a permanent price table; capture the pricing page date and applicable commercial terms.
• Separate Chat input/output, Embed input, Rerank search, and platform infrastructure costs.
• Newer model access and production capacity can have account-specific terms.
• A cheaper model or smaller context is acceptable only after representative evaluation passes.

Authentication

Use an environment-specific key injected from an approved secret manager. Never print, persist, commit, or place CO_API_KEY in an example. Confirm access with the least costly bounded operation appropriate to the task, and treat key creation, rotation, revocation, role changes, and production-capacity requests as owner-approved actions.

Instructions

1. Collect current public prices or contracted rates with effective date and currency.
2. Measure per-request input, output, Embed, Rerank, retry, cache, and failure units by workload class.
3. Forecast baseline, expected, and peak volume with capacity and retry assumptions.
4. Evaluate model routing, context reduction, retrieval pruning, batching, caching, and duplicate suppression.
5. Set per-tenant and global budgets, alerts, admission controls, and an owner-approved degradation path.
6. Reconcile forecast with actual billing regularly and investigate material variance.

Approval Boundaries

Do not expose or rotate keys, change Cohere Team roles, accept commercial terms, enable sensitive production data, increase spend or capacity, switch production models, send a support bundle, or execute model-proposed side effects without the accountable owner's approval. Keep diagnosis read-only unless implementation was requested.

Output

Return the resolved API and model contract, files or settings inspected, evidence collected, validation result, remaining risk, owner, and rollback or next action. Redact keys, authorization headers, prompts, retrieved documents, embeddings, customer identifiers, and unrestricted environment output.

Error Handling

Condition	Response
Unknown contract rate	Mark the forecast incomplete and obtain the governing rate.
Quality below floor	Reject the savings change.
Retry amplification	Fix failure handling and include wasted units in the model.
Budget exceeded	Apply approved admission control; do not silently weaken safety.

Examples

Use this compact handoff shape to keep the selected scope, validation evidence, and operational result reviewable.

Input:

workload=rag-chat; monthly-requests=100000; prices=effective-date-snapshot

Expected handoff:

forecast=three-scenarios; unit-cost=measured; controls=enabled; quality=pass

Resources

• Skill-specific official documentation
• Pricing
• Rate limits