container-density-audit

النجوم٤

التفرعات٠

آخر تحديث١٨ يوليو ٢٠٢٦ في ١٥:٢٥

Audit Kubernetes/container clusters for poor bin-packing — inflated resource requests vs actual usage, low node utilization, missing autoscaling, pods hoarding capacity. Use this skill whenever the user shares k8s manifests, kubectl top / metrics-server output, cluster utilization data, or asks about node counts, pod density, cluster efficiency, or Kubernetes sustainability. Part of Lean Agentic AI Skills; emits lean-findings.json.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

المصدر

navveenb

navveenb/lean-agentic-ai-skills

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مديرو الشبكات وأنظمة الحاسوبمهن الحاسوب والرياضيات·SOC 15-1244

مستكشف الملفات

2 ملفات

SKILL.md

readonly

name

container-density-audit

description

Container Density Audit

Producer skill. Input: manifests (requests/limits), kubectl top nodes/pods output, cluster autoscaler config, node inventory. Output: lean-findings.json.

In Kubernetes the scheduler believes requests, not reality. Every pod requesting 4x what it uses forces the cluster to run nodes that are "full" on paper and idle in silicon. Density is the M-lever: fewer nodes for the same work.

Subject type: emit subject.type: "container-platform" in findings.

Signatures

Request inflation — usage-to-request ratio < 30% across a workload's pods. High. Evidence: actual vs requested from metrics. Fix: lower requests (state the new value and the headroom retained) or install VPA in recommend mode first.
Missing requests entirely — scheduler flying blind; density unmanageable. Medium-high.
Limits == requests everywhere for burstable workloads — no bursting, forced oversizing. Medium (context-dependent — Guaranteed QoS may be deliberate; ask/flag rather than assert).
Node utilization — fleet p95 CPU/mem below ~50% with stable pod count → node pool downsizing or consolidation candidate. High.
No cluster autoscaler / no scale-down — scale-down-disabled annotations everywhere, generous over-provisioning buffers. Medium.
System overhead — daemonsets summing to a large slice of small nodes → fewer, larger nodes often pack better. Low-medium; name the trade-off (blast radius).
Missing PodDisruptionBudgets blocking consolidation — autoscaler can't drain. Low.

Honesty rules

Bin-packing tighter trades against noisy-neighbor risk and surge headroom; every density recommendation states retained headroom. Never recommend request cuts from a utilization window that misses known peaks — say what the window was.

Cost signal (countable)

Cost driver: node-hours (or the cluster's billed capacity unit). cost_signal.observed names the current node fleet size and utilisation, direction reflects the consolidation opportunity qualitatively.

Not this skill's job

VM-level sizing (right-size-compute), whether workloads belong on k8s at all (vm-to-serverless-advisor).

المزيد من هذا المستودع

نفس المستودع

agentic-loop-auditor

navveenb/lean-agentic-ai-skills

Audit agent frameworks and agentic workflows for token-burning loop pathologies — unbounded retries, excessive planning depth, tool-call thrashing, context snowballing, missing loop budgets and kill switches. Use this skill whenever the user shares agent configs or traces (LangChain/LangGraph, CrewAI, AutoGen, custom loops), complains an agent is slow or expensive, or is designing any autonomous/multi-step agent. Part of Lean Agentic AI Skills; emits lean-findings.json.

2026-07-184

ai-eval-efficiency-audit

navveenb/lean-agentic-ai-skills

Audit AI evaluation infrastructure for wasted compute — full benchmark suites re-run on unchanged cases, LLM-as-judge grading without caching, oversized judge models, redundant eval passes per commit, and missing result reuse. Use this skill whenever the user shares eval harness configs or CI eval steps, complains that evals are slow or expensive, mentions LLM-as-judge costs, or runs benchmark suites on every change. Part of Lean Agentic AI Skills; emits lean-findings.json.

2026-07-184

api-efficiency-audit

navveenb/lean-agentic-ai-skills

Audit API design for chattiness and over-fetching — REST endpoints requiring N calls per screen, GraphQL resolvers with N+1 fan-out, default-everything payloads, missing pagination and field selection, provider-side polling where webhooks serve, and uncompressed responses. Use this skill whenever the user shares API schemas (OpenAPI, GraphQL SDL), resolver code, endpoint designs, or asks why clients make too many calls or payloads are heavy. Part of Lean Agentic AI Skills; emits lean-findings.json.

2026-07-184

caching-strategy-designer

navveenb/lean-agentic-ai-skills

Design caching layers so work is done once, not per request — HTTP/CDN caching, application-level caches (Redis/Memcached), computed-result caching, and cache-invalidation strategy. Use this skill whenever the user wants to add caching, asks why the same work is recomputed, designs a read-heavy system, or an audit skill (db-efficiency-audit, web-carbon-audit) flags uncached repeated work. Part of Lean Agentic AI Skills; emits a cache design plus lean-findings.json.

2026-07-184

carbon-aware-scheduler

navveenb/lean-agentic-ai-skills

Design time-shifting of flexible workloads (batch jobs, CI, training, backups, crons) to hours and regions when grid electricity is cleanest, using carbon-intensity signals like Electricity Maps, WattTime, or the Carbon Aware SDK. Use this skill whenever the user mentions carbon-aware computing, scheduling flexible/deferrable jobs, demand shifting, running workloads when energy is clean, or asks how to reduce the carbon intensity (not just amount) of their compute. Part of Lean Agentic AI Skills; emits schedule designs plus lean-findings.json.

2026-07-184

carbon-budget-designer

navveenb/lean-agentic-ai-skills

Design per-feature and per-service efficiency budgets — page-weight budgets, token budgets per AI feature, query budgets per endpoint, GPU-hour budgets per training project — with owners, review checkpoints, and enforcement hooks. Use this skill whenever the user wants sustainability targets for teams, asks how to keep efficiency from regressing organizationally, mentions performance-budget-style governance for carbon/energy, or is setting engineering standards. Part of Lean Agentic AI Skills; an advisor emitting budget definitions plus lean-findings.json.

2026-07-184

name

container-density-audit

description

Container Density Audit

Producer skill. Input: manifests (requests/limits), kubectl top nodes/pods output, cluster autoscaler config, node inventory. Output: lean-findings.json.

Subject type: emit subject.type: "container-platform" in findings.

Signatures

Request inflation — usage-to-request ratio < 30% across a workload's pods. High. Evidence: actual vs requested from metrics. Fix: lower requests (state the new value and the headroom retained) or install VPA in recommend mode first.
Missing requests entirely — scheduler flying blind; density unmanageable. Medium-high.
Limits == requests everywhere for burstable workloads — no bursting, forced oversizing. Medium (context-dependent — Guaranteed QoS may be deliberate; ask/flag rather than assert).
Node utilization — fleet p95 CPU/mem below ~50% with stable pod count → node pool downsizing or consolidation candidate. High.
No cluster autoscaler / no scale-down — scale-down-disabled annotations everywhere, generous over-provisioning buffers. Medium.
System overhead — daemonsets summing to a large slice of small nodes → fewer, larger nodes often pack better. Low-medium; name the trade-off (blast radius).
Missing PodDisruptionBudgets blocking consolidation — autoscaler can't drain. Low.

Honesty rules

Cost signal (countable)

Not this skill's job

VM-level sizing (right-size-compute), whether workloads belong on k8s at all (vm-to-serverless-advisor).