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Infrastructure · Version 1.6.0 · Reviewed 2026-08-02

Linux Performance Investigator

Review and harden CPU saturation analysis and I/O latency diagnosis with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Builds evidence-driven Linux performance investigations using CPU, memory, disk, network, scheduler, pressure, and eBPF signals.

₹99 one-time

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What this skill helps you do

  • CPU saturation analysis
  • I/O latency diagnosis
  • Memory-pressure investigation

How Linux Performance Investigator works

You provide

Schema, query plans, and the real access pattern

It inspects

Plan accuracy and lock behavior for CPU saturation analysis

It decides

A I/O latency diagnosis change weighed against write cost

You verify

Re-measured plan with buffer reads and timing compared

What it checks first

Linux Performance Investigator builds evidence-driven Linux performance investigations using CPU, memory, disk, network, scheduler, pressure, and eBPF signals. Use it when the work involves CPU saturation analysis, I/O latency diagnosis, Memory-pressure investigation.

  1. The actual query plan with real row counts, not the estimated plan or the query text alone.
  2. Whether the workload is read-heavy, write-heavy, or mixed, since the correct design differs sharply.
  3. Transaction boundaries and duration, because long transactions block vacuum and hold locks.
  4. Index coverage relative to both the filter and the sort, since satisfying one but not the other still costs a sort.
  5. Connection pool behavior, as pool exhaustion presents as database slowness while the database is idle.

Failure modes it recognizes

  • An index that serves the predicate but not the ordering, forcing a full sort for a small LIMIT.
  • A long-running transaction preventing vacuum and causing gradual bloat and plan degradation.
  • Implicit type casting on a join or filter column silently disabling index use.
  • Connection pool exhaustion from long-held connections, appearing as a database problem.
  • A write-heavy table with excessive indexes where insert cost dominates the workload.
  • Statistics stale after a bulk load, so the planner chooses a plan for a table size that no longer exists.

Answers it will reject

  • Adding an index per slow query until write amplification becomes the new bottleneck.
  • Tuning configuration parameters before examining the plan for the dominant query.
  • Interpreting `EXPLAIN` without `ANALYZE`, which reports estimates and proves nothing.
  • Increasing pool size to fix latency caused by lock contention, which adds waiters rather than capacity.

Decision rules it applies

  • Optimize the query that dominates total time, not the one that feels slowest in isolation.
  • Order composite index columns by equality first, then range or sort last.
  • Keep transactions short and never hold one open across an external call.
  • Create and drop indexes concurrently on live tables, accepting the longer build for the absent lock.

Evidence it asks for

  • `EXPLAIN (ANALYZE, BUFFERS)` to compare estimated with actual rows and attribute I/O.
  • Rank queries by cumulative execution time rather than by single-execution latency.
  • Monitor the oldest open transaction and lock wait counts as standing metrics.

The method inside

  1. Define the measured baseline and user-visible target for CPU saturation analysis.
  2. Attribute the dominant cost or latency mechanism affecting I/O latency diagnosis.
  3. Rank memory-pressure investigation changes by expected impact, confidence, effort, and regression risk.
  4. Validate under representative load and retain guardrail metrics that detect a shifted bottleneck.

Deliverables

  • CPU saturation analysis assessment
  • I/O latency diagnosis decision and action plan
  • Memory-pressure investigation verification checklist

Evidence requirements

  • Infrastructure code or configuration
  • Runtime topology and environment constraints
  • Plan, events, policies, and failure symptoms

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Load average is high but CPU utilization is low. What data should I collect before changing instance size?

Expected output

High load includes tasks waiting in uninterruptible I/O. Check pressure stall information, disk latency, blocked-task stacks, and network filesystem health before assuming compute shortage...

Boundaries and compatibility

Ideal for

  • CPU saturation analysis: produce a decision or artifact grounded in supplied evidence.
  • I/O latency diagnosis: produce a decision or artifact grounded in supplied evidence.
  • Memory-pressure investigation: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Applying infrastructure changes without approval
  • Assuming cloud access or live resource visibility

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.