openevidence-performance-tuning
Improves latency for OpenEvidence clinical queries through strategic caching and citation batching.
Install
mkdir -p .claude/skills/openevidence-performance-tuning && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4797" && unzip -o skill.zip -d .claude/skills/openevidence-performance-tuning && rm skill.zipInstalls to .claude/skills/openevidence-performance-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.
Performance Tuning for OpenEvidence.Key capabilities
- →Cache evidence responses with a 30-minute TTL
- →Cache citation metadata with a 1-hour TTL
- →Batch citation retrieval in groups of 25 with 500ms pauses
- →Enable HTTP keep-alive for persistent API connections
- →Monitor average query latency
- →Set client timeout to 30s for complex multi-condition queries
How it works
The skill implements caching strategies for evidence summaries and citation batching to reduce system load. It optimizes query specificity and request handling for large-scale medical data retrieval.
Inputs & outputs
When to use openevidence-performance-tuning
- →Implement response caching for medical evidence queries
- →Batch citation fetches to reduce API overhead
- →Optimize query performance for clinical AI tools
- →Set TTL intervals for evidence and citation data
About this skill
OpenEvidence Prompt Refinement
Overview
Tune context and question structure while holding clinical accountability and source review constant. 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
- The official guide publishes prompt guidance and dedicated workflows for complex cases and Snow.
- More detail is not always safer; include only relevant, authorized context.
- Performance means decision usefulness and evidence traceability, not fastest answer or longest response.
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
- State the decision, intended user, population, outcome, constraints, and what uncertainty must remain visible.
- Remove identifiers and irrelevant narrative; separate known facts from assumptions.
- Run a baseline synthetic or authorized de-identified question and score relevance, citations, applicability, and reviewer effort.
- Change one prompt element at a time: specificity, timeframe, comparator, output structure, or request for conflicting evidence.
- Open citations and have a qualified clinician compare versions using the same rubric.
- Save a reusable pattern only if it improves the defined outcome across multiple representative cases.
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 |
|---|---|
| Prompt becomes leading | Restore neutral framing and request alternatives or conflicting evidence. |
| Answer gets longer, not better | Optimize for reviewable claims and cited evidence. |
| Case is urgent | Use the clinical emergency workflow, not prompt iteration. |
Examples
This compact example shows the minimum reviewable handoff; adapt fields to the approved workflow without adding sensitive data.
Input:
decision=diagnostic workup; context=de-identified; variants=3; reviewer=clinician
Expected handoff:
best-variant=2; traceability=improved; uncertainty=preserved; template=approved
Resources
When not to use it
- →When evidence summaries are stale due to a cache that is too long for rapidly evolving topics
- →When a study is not yet indexed, resulting in missing citations
Limitations
- →Slow evidence queries can occur with broad multi-condition searches
- →Rate limits can be hit with too many parallel citation fetches
- →Complex queries may time out if multi-study synthesis exceeds limits
How it compares
This skill explicitly defines TTLs for evidence and citations, batches citation fetches with pauses, and uses connection pooling, unlike generic performance tuning.
Compared to similar skills
openevidence-performance-tuning side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| openevidence-performance-tuning (this skill) | 1 | 2mo | No flags | Intermediate |
| exa-performance-tuning | 3 | 2mo | Review | Intermediate |
| perplexity-rate-limits | 0 | 2mo | No flags | Intermediate |
| documenso-performance-tuning | 0 | 2mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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