openevidence-cost-tuning
Strategies for monitoring and reducing OpenEvidence API spending through caching and request batching.
Install
mkdir -p .claude/skills/openevidence-cost-tuning && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/5367" && unzip -o skill.zip -d .claude/skills/openevidence-cost-tuning && rm skill.zipInstalls to .claude/skills/openevidence-cost-tuning
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Cost Tuning for OpenEvidence.Key capabilities
- →Cache frequent API calls
- →Batch API requests
- →Monitor API call frequency
How it works
It provides strategies for reducing API costs through caching and batching, accompanied by a simple tracking wrapper to monitor call volume.
Inputs & outputs
When to use openevidence-cost-tuning
- →Reducing API consumption costs
- →Implementing usage budgets
- →Optimizing DeepConsult spending
- →Monitoring API call frequency
About this skill
OpenEvidence Access and Value Review
Overview
Build a decision-ready value review from authorized local evidence and current commercial terms. Keep inputs minimal, separate observed facts from assumptions, and leave consequential decisions with the named accountable owner.
Prerequisites
- A clearly bounded workflow, accountable clinical owner, and organizational policy
- Current first-party OpenEvidence documentation and applicable institution agreements
- Synthetic or properly authorized minimum-necessary data
Tool Discipline
Use Read, Glob, and Grep to inspect supplied policies, plans, and evidence. Use WebFetch only for current first-party OpenEvidence documentation. Use Write or Edit only when the user requests a named deliverable with an approved destination. Never expose credentials, PHI, recordings, or unrestricted environment output.
Current Contract
- Public product pages do not establish institution-specific price, quota, SLA, or entitlement terms.
- Only a current order form, MSA, or authorized account surface can establish commercial commitments.
- Clinical quality and safety outcomes must not be reduced to raw query volume.
Authentication
Use only the official OpenEvidence web/mobile sign-in or an institution-approved access path. Do not invent API keys, OAuth clients, SDK credentials, service accounts, or private endpoints. Never ask a user to reveal a password, session token, cookie, or recovery code.
Instructions
- Define the decision: pilot continuation, feature expansion, workflow retirement, or contract review.
- Collect authorized evidence for active users, completed workflows, time saved, failure modes, and training/support burden.
- Separate product facts from institution-specific contract terms and unverified anecdotes.
- Measure value by workflow outcome, evidence-review quality, clinician time, and adoption friction; avoid patient-outcome causality claims.
- Identify unused or duplicative workflows and test a reversible change with the accountable owner.
- Return a value brief with data provenance, limitations, owner, decision date, and next measurement.
Approval Boundaries
Do not create or share accounts; change access, roles, agreements, consent, retention, or security settings; enter PHI; record a conversation; copy content into another system; contact a patient; make a diagnosis or treatment decision; submit billing; transmit a support packet; run a production pilot; or represent vendor capabilities without explicit approval from the accountable owner. A qualified professional remains responsible for clinical decisions.
Output
Return scope, current first-party evidence and date, data classification, workflow or findings, citations reviewed, assumptions rejected, clinical and governance owners, approval state, unresolved risk, and the exact next action. Redact patient and credential data.
Error Handling
| Condition | Response |
|---|---|
| No usage export | Use a documented sample or local survey and label the evidence incomplete. |
| Price unavailable | Request the current commercial document; never estimate it as fact. |
| Metric encourages unsafe speed | Replace it with quality, review, and workflow-completion measures. |
Examples
This compact example shows the minimum reviewable handoff; adapt fields to the approved workflow without adding sensitive data.
Input:
period=90d; cohort=pilot clinicians; decision=renewal; contract=owner-held
Expected handoff:
value=evidence-backed; gaps=2; commercial-terms=unverified; recommendation=conditional
Resources
How it compares
This approach focuses on programmatic cost reduction strategies rather than just monitoring billing dashboards.
Compared to similar skills
openevidence-cost-tuning side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| openevidence-cost-tuning (this skill) | 1 | 2mo | No flags | Beginner |
| billing-automation | 10 | 4mo | No flags | Intermediate |
| plaid-fintech | 3 | 8mo | No flags | Advanced |
| domain-fintech | 1 | 8mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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