Auto-refresh KB pricing, align skill with Anthropic best practices
Why this set of changes:
- KB pricing was drifting silently — domain files (database.yaml,
storage.yaml, etc.) had prices 30-800% off the live Oracle API and
nobody read them. The skill was auditing as stale on every check
with no path to fix it.
- The skill itself violated Anthropic's spec (`name` field had
uppercase/spaces) and was over the 500-line guideline (647 lines),
hurting discovery and load performance.
- Welcome flow occasionally improvised the menu instead of reading
SKILL.md, missing options.
Pricing — single source of truth, fully automated:
- Extend tools/refresh_sku_catalog.py with --refresh-domain compute,
pulls shape-level prices from the Oracle public pricing API
(apexapps.oracle.com), preserves manual fields (notes, GPU specs,
free-tier annotations, estimation_helpers), recomputes derived
monthly values, and protects $0 free-tier prices from overwrite.
- Delete 12 redundant pricing/<domain>.yaml files. They duplicated
oci-sku-catalog.yaml with worse abstractions and were nobody's
source of truth (no tool consumed them).
- Migrate the genuinely valuable knowledge from those 12 files
(billing models, BYOL rules, free-tier rules, ECPU vs OCPU,
X11M elastic model, hyperscaler comparisons, service nuances)
into kb/field-knowledge/pricing-knowledge.yaml — non-numeric,
no refresh needed.
- Result: pricing freshness check goes from 13 stale files to 0.
KB freshness automation:
- Add tools/kb_freshness.py — wrapper around kb_linter.check_freshness()
with --check, --auto-refresh, --json, --quiet modes. Bridges stale
files to their refresh tools (SKU catalog, compute domain, arch
center). Wired into the welcome flow as a pre-flight banner that
asks the user before refreshing.
- Fix pre-existing kb_linter bug: it crashed on the 45 multi-doc
YAML files (frontmatter + body pattern) because it used safe_load
instead of safe_load_all. Freshness check was effectively dead.
- Standardize timestamp field: linter now accepts last_verified,
last_updated, and last_refreshed; refresh_arch_catalog writes
last_verified instead of last_refreshed.
- Add make freshness / make freshness-refresh targets.
Skill alignment with Anthropic Agent Skills best practices:
- Rename `name: OCI Deal Accelerator` to `oci-deal-accelerator`
to comply with the [a-z0-9-]{1,64} spec.
- Refactor SKILL.md from 674 to 445 lines via progressive disclosure:
extract WA review output format, ECAL readiness format, and output
conventions into docs/skill/*.md referenced from the main file.
- Add scripts/sync-skill.py + make sync-skill: source of truth is
root SKILL.md, .agents/skills/oci-deal-accelerator/SKILL.md is
auto-generated. make lint validates sync.
- Add evaluations/ with 3 manual baseline scenarios (welcome-flow,
full-proposal, wa-review) per the Anthropic best-practices guidance
to "build evaluations first."
Welcome flow hardening:
- Tighten CLAUDE.md to MANDATE reading SKILL.md before showing the
menu (no improvising), and document the freshness pre-flight check
with the ask-before-refresh user flow.
- Update SKILL.md welcome flow to instruct: parse kb_freshness JSON,
show banner with stale count + oldest file, prompt user to refresh
(only when an automated tool exists), fall back silently on errors.
Linter hygiene (zero remaining issues):
- Expand config/kb-tags.yaml taxonomy with features, operations,
metrics, limitations sections covering 31 previously-unknown tags
used in field findings (rac, ecpu, refreshable-clone, hnsw, etc.).
- Assign owners for kb/compatibility/, kb/competitive/,
kb/well-architected/ (Diego Cabrera as default until team grows);
kb/pricing/ marked as "Auto-refreshed" since it no longer needs
human ownership.
- kb_linter accepts top-level `date` as fallback for contributor
block; migrate FF-202603-008 from legacy `reported_by` to
contributor block.
- Result: linter goes from 45 issues to 0.
Other:
- Recompute estimation_helpers monthly values in compute.yaml after
the price refresh (they were derived from the old E5/A1 numbers).
- Add kb/README.md — contributor guide (directory map, frontmatter
spec, refresh tooling, review cadence).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
root
@@ -1,191 +1,160 @@
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---
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last_verified: 2025-09-11
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source: https://www.oracle.com/cloud/price-list/
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description: OCI Compute pricing for estimation purposes.
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Prices are approximate and subject to change. Always verify with
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OCI pricing calculator for final quotes.
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last_verified: '2026-04-08'
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source: https://apexapps.oracle.com/pls/apex/cetools/api/v1/products/
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description: OCI Compute pricing for estimation purposes. Auto-refreshed from the
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Oracle public pricing API by tools/refresh_sku_catalog.py --refresh-domain compute.
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GPU shapes, secure desktops, estimation_helpers, and discounts are NOT auto-refreshed.
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currency: USD
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---
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# ── Virtual Machine Instances ──────────────────────────────────────
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flexible_shapes:
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VM.Standard.E6.Flex:
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ocpu_per_hour: 0.0168
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memory_per_gb_hour: 0.0011
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ocpu_per_hour: 0.03
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memory_per_gb_hour: 0.002
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monthly_730h:
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ocpu: 12.26
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memory_per_gb: 0.80
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notes: "AMD EPYC (latest gen), best price/performance for general workloads"
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ocpu: 21.9
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memory_per_gb: 1.46
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notes: AMD EPYC (latest gen), best price/performance for general workloads
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VM.Standard.E5.Flex:
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ocpu_per_hour: 0.0210
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memory_per_gb_hour: 0.0014
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ocpu_per_hour: 0.03
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memory_per_gb_hour: 0.002
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monthly_730h:
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ocpu: 15.33
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memory_per_gb: 1.02
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notes: "AMD EPYC, general-purpose workloads"
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ocpu: 21.9
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memory_per_gb: 1.46
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notes: AMD EPYC, general-purpose workloads
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VM.Standard.E4.Flex:
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ocpu_per_hour: 0.0252
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memory_per_gb_hour: 0.0017
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ocpu_per_hour: 0.025
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memory_per_gb_hour: 0.0015
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monthly_730h:
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ocpu: 18.40
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memory_per_gb: 1.24
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notes: "AMD EPYC (previous gen)"
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ocpu: 18.25
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memory_per_gb: 1.09
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notes: AMD EPYC (previous gen)
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VM.Standard.E3.Flex:
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ocpu_per_hour: 0.0252
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memory_per_gb_hour: 0.0017
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ocpu_per_hour: 0.025
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memory_per_gb_hour: 0.0015
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monthly_730h:
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ocpu: 18.40
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memory_per_gb: 1.24
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notes: "AMD EPYC (older gen)"
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ocpu: 18.25
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memory_per_gb: 1.09
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notes: AMD EPYC (older gen)
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VM.Standard3.Flex:
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ocpu_per_hour: 0.0336
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memory_per_gb_hour: 0.0022
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monthly_730h:
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ocpu: 24.53
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memory_per_gb: 1.61
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notes: "Intel Xeon, for Intel-optimized workloads"
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notes: Intel Xeon, for Intel-optimized workloads
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VM.Standard.A2.Flex:
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ocpu_per_hour: 0.0254
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memory_per_gb_hour: 0.0017
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ocpu_per_hour: 0.014
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memory_per_gb_hour: 0.002
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monthly_730h:
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ocpu: 18.54
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memory_per_gb: 1.24
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notes: "Ampere Arm (latest A2), best cost for Arm-compatible workloads"
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ocpu: 10.22
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memory_per_gb: 1.46
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notes: Ampere Arm (latest A2), best cost for Arm-compatible workloads
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VM.Standard.A1.Flex:
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ocpu_per_hour: 0.0127
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memory_per_gb_hour: 0.0008
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monthly_730h:
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ocpu: 9.27
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memory_per_gb: 0.58
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free_tier: "3,000 OCPU-hours + 18,000 GB-hours per month"
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notes: "Ampere Arm A1, eligible for Always Free tier"
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free_tier: 3,000 OCPU-hours + 18,000 GB-hours per month
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notes: Ampere Arm A1, eligible for Always Free tier
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VM.Standard.x86.Generic:
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ocpu_per_hour: 0.0252
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memory_per_gb_hour: 0.0017
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notes: "Generic x86 pricing when specific shape not yet selected"
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notes: Generic x86 pricing when specific shape not yet selected
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VM.Optimized3.Flex:
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ocpu_per_hour: 0.0504
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memory_per_gb_hour: 0.0034
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ocpu_per_hour: 0.054
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memory_per_gb_hour: 0.0015
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monthly_730h:
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ocpu: 36.79
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memory_per_gb: 2.48
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notes: "Highest single-thread performance"
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# ── Bare Metal Instances ───────────────────────────────────────────
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ocpu: 39.42
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memory_per_gb: 1.09
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notes: Highest single-thread performance
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bare_metal_shapes:
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BM.Standard.E5:
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ocpu_per_hour: 0.0168
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notes: "AMD EPYC bare metal"
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ocpu_per_hour: 0.03
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notes: AMD EPYC bare metal
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BM.Standard.E4:
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ocpu_per_hour: 0.0210
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notes: "AMD EPYC bare metal (previous gen)"
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ocpu_per_hour: 0.025
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notes: AMD EPYC bare metal (previous gen)
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BM.Standard.E3:
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ocpu_per_hour: 0.0420
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notes: "AMD EPYC bare metal (older gen)"
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ocpu_per_hour: 0.025
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notes: AMD EPYC bare metal (older gen)
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BM.Standard3:
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ocpu_per_hour: 0.0504
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notes: "Intel Xeon bare metal"
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notes: Intel Xeon bare metal
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BM.Standard.A1:
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ocpu_per_hour: 0.0127
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notes: "Ampere Arm bare metal"
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notes: Ampere Arm bare metal
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BM.Standard.x9:
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ocpu_per_hour: 0.0672
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notes: "Intel Xeon x9 bare metal"
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# ── GPU Accelerated Compute ────────────────────────────────────────
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ocpu_per_hour: 0.04
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notes: Intel Xeon x9 bare metal
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gpu_shapes:
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# NVIDIA Blackwell (latest)
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BM.GPU.B300:
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per_gpu_hour: 26.88
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gpu_count: 8
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gpu_model: "NVIDIA B300"
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notes: "Blackwell latest, HPC/AI training"
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gpu_model: NVIDIA B300
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notes: Blackwell latest, HPC/AI training
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BM.GPU.B200:
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per_gpu_hour: 20.16
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gpu_count: 8
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gpu_model: "NVIDIA B200"
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notes: "Blackwell, large-scale training"
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# NVIDIA Hopper
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gpu_model: NVIDIA B200
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notes: Blackwell, large-scale training
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BM.GPU.H200:
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per_gpu_hour: 18.54
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gpu_count: 8
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gpu_model: "NVIDIA H200"
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notes: "Hopper H200, large model training"
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gpu_model: NVIDIA H200
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notes: Hopper H200, large model training
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BM.GPU.H100:
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per_gpu_hour: 12.96
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gpu_count: 8
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gpu_model: "NVIDIA H100"
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notes: "Hopper H100, training workloads"
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# NVIDIA Ampere / Ada
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gpu_model: NVIDIA H100
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notes: Hopper H100, training workloads
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BM.GPU.A100:
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per_gpu_hour: 6.72
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gpu_count: 8
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gpu_model: "NVIDIA A100 40GB/80GB"
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notes: "Training and inference, 40GB or 80GB HBM2e"
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gpu_model: NVIDIA A100 40GB/80GB
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notes: Training and inference, 40GB or 80GB HBM2e
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VM.GPU.A10.1:
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per_gpu_hour: 0.72
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gpu_count: 1
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gpu_model: "NVIDIA A10"
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notes: "Inference workloads, cost-effective"
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gpu_model: NVIDIA A10
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notes: Inference workloads, cost-effective
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VM.GPU.A10.2:
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per_gpu_hour: 0.72
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gpu_count: 2
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gpu_model: "NVIDIA A10"
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notes: "Inference workloads"
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gpu_model: NVIDIA A10
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notes: Inference workloads
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BM.GPU.L40S:
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per_gpu_hour: 2.42
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gpu_count: 4
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gpu_model: "NVIDIA L40S"
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notes: "Ada Lovelace, inference and graphics"
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# ── Secure Desktops ───────────────────────────────────────────────
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gpu_model: NVIDIA L40S
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notes: Ada Lovelace, inference and graphics
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secure_desktops:
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per_desktop_month: 1750.00
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notes: "Managed virtual desktop infrastructure"
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# ── Estimation Helpers ─────────────────────────────────────────────
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per_desktop_month: 1750.0
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notes: Managed virtual desktop infrastructure
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estimation_helpers:
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typical_app_server:
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config: "VM.Standard.E5.Flex, 4 OCPU, 64 GB"
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monthly: 126.60 # (4 * 15.33) + (64 * 1.02)
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config: VM.Standard.E5.Flex, 4 OCPU, 64 GB
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monthly: 181.04
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typical_web_server:
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config: "VM.Standard.E5.Flex, 2 OCPU, 16 GB"
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monthly: 46.98 # (2 * 15.33) + (16 * 1.02)
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config: VM.Standard.E5.Flex, 2 OCPU, 16 GB
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monthly: 67.16
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typical_bastion:
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config: "VM.Standard.E5.Flex, 1 OCPU, 8 GB"
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monthly: 23.49 # (1 * 15.33) + (8 * 1.02)
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config: VM.Standard.E5.Flex, 1 OCPU, 8 GB
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monthly: 33.58
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typical_arm_app_server:
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config: "VM.Standard.A1.Flex, 4 OCPU, 24 GB"
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monthly: 50.96 # (4 * 9.27) + (24 * 0.58)
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config: VM.Standard.A1.Flex, 4 OCPU, 24 GB
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monthly: 51.1
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discounts:
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preemptible: "~50% off on-demand"
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capacity_reservation: "~15% off on-demand (85% of list)"
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reserved_1yr: "~35% off on-demand"
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reserved_3yr: "~55% off on-demand"
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universal_credits: "Volume discounts on committed spend"
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preemptible: ~50% off on-demand
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capacity_reservation: ~15% off on-demand (85% of list)
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reserved_1yr: ~35% off on-demand
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reserved_3yr: ~55% off on-demand
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universal_credits: Volume discounts on committed spend
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os_pricing:
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free:
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- "Oracle Linux"
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- "CentOS"
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- "Ubuntu"
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- "Oracle Autonomous Linux"
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- Oracle Linux
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- CentOS
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- Ubuntu
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- Oracle Autonomous Linux
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paid:
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windows_server:
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notes: "Pricing varies by edition, added to compute cost"
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notes: Pricing varies by edition, added to compute cost
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