--- name: mindtickle-performance-tuning-04fc63e9fe2b description: 'Diagnose and improve a Mindtickle integration with measured latency, backlog, freshness, and reconciliation evidence. Use when reports or syncs miss objectives. Trigger with "tune Mindtickle performance".' argument-hint: "[service] [measurement-window]" allowed-tools: Read, Glob, Grep, WebFetch, Write, Edit version: 1.8.0 author: Jeremy Longshore license: MIT tags: [saas, mindtickle, performance, observability, reliability] model: inherit effort: high compatibility: Designed for Claude Code; production load, concurrency, caching, and schedule changes require service and tenant-owner approval --- # Evidence-Driven Mindtickle Integration Performance ## Overview Improve the customer-controlled integration path from a reproducible baseline while respecting tenant contracts, privacy, freshness, and correctness. ## Prerequisites - A service-level objective, representative workload, current contract digest, and observation window - Metrics for latency, attempts, errors, retries, backlog age, throughput, freshness, and reconciliation - A non-production load path or approved canary with explicit abort thresholds ## Tool Discipline Use `Read`, `Glob`, and `Grep` to inspect traces and configuration, `WebFetch` for current authorized contracts, and `Write` or `Edit` for benchmarks, controlled changes, tests, and redacted receipts. ## Current Contract Performance spans the customer application, network, identity, adapter, tenant operations, and downstream systems. Mindtickle states support for scalable integrations but does not publish universal latency or quota targets, so customer objectives and tenant-specific evidence must remain separate. ## Authentication Benchmark with synthetic or aggregate data and a least-privilege non-production principal. Never use extra credentials to create artificial concurrency or include payload contents in traces. ## Instructions 1. Define the user or business symptom, objective, correctness invariant, workload, and baseline window. 2. Break elapsed time into queue, customer processing, network, tenant response, downstream processing, and reconciliation. 3. Segment by operation, page or batch, payload class, tenant, response class, and time without exposing personal data. 4. Rank bottlenecks by measured contribution and distinguish latency from throttling, backlog, and data-quality delay. 5. Propose one controlled change at a time: scheduling, bounded concurrency, pagination, batching, caching, indexing, or payload reduction only where the contract permits it. 6. Define cache ownership, freshness, invalidation, privacy, and stale-data behavior before enabling a cache. 7. Run the experiment within abort thresholds and compare p50, tail latency, errors, retries, backlog, freshness, and reconciliation. 8. Retain the change only when the improvement is repeatable and correctness is unchanged; otherwise roll back. ## Approval Boundaries Do not load-test production, raise concurrency, retain learner data in a cache, or trade correctness for speed without explicit owners. ## Output Return the objective, baseline, bottleneck evidence, experiment, approvals, before-and-after metrics, correctness reconciliation, decision, and rollback receipt. ## Error Handling | Condition | Response | |---|---| | Metrics omit queue or retry time | Repair instrumentation before claiming improvement. | | Vendor behavior changes during a test | Stop, preserve the window, and rerun after the contract is stable. | | Faster run produces divergent data | Roll back immediately and investigate correctness first. | ## Example ```text objective=freshness-under-target; baseline=7d; bottleneck=customer-queue; change=schedule-partition; tail-latency=improved; reconciliation=exact ``` ## Resources - [Mindtickle integrations](https://www.mindtickle.com/platform/integrations/) - [Mindtickle Service Level Agreement](https://www.mindtickle.com/legal/service-level-agreement/) ## Next Steps Observe the retained change for a full workload cycle and update the capacity policy with measured evidence. --- Source attribution: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/mindtickle-pack/skills/mindtickle-performance-tuning/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2025 Jeremy Longshore Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/mindtickle-pack/skills/mindtickle-performance-tuning/SKILL.md Attribution basis: Source repository record Fields of work: Sales Roles: Customer Success Manager, Sales Operations, Sales Development Rep (SDR) Specialties: API, Backend Tools: Mindtickle, Redis Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 970838458fcc25a154c54ec0b7b131d88dc8f45d52fe6739659de04faddd4833