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Productionize ML models and LLM features: deployment specs, drift monitoring, RAG design and cost control. Use when deploying a model, building RAG, or monitoring drift.

borghei/Claude-Skills Agent Skills author: borghei 13 files · 3 scripts body ≈ 2 686 tokens Open the sourcegithub.com↗ analyzed 7 h ago

Productionize ML models and LLM features: deployment specs, drift monitoring, RAG design and cost control.

As a process D 48/100 · Unfinished process — References files that are not bundled: references/*.md

ProcedureKubernetesDockerInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
82
Run on models
none yet
Process rating
D
48/100
Unfinished process
References files that are not bundled: references/*.md
Tools and files w 18
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security references/tool-reference.md:37
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | `k8s.yaml` | Deployment (rolling update with `maxUnavailable: 0`, startup, liveness and readiness probes, requests and limits, `runAsNonRoot`, no privilege escalation, all capabilities dropped), Clu

Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/*.md

Process rating: all ten parameters 48/100

Will not run. References files that are not bundled: references/*.md
  • 0Tools and files. 1 referenced file(s) missing: references/*.md
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 23 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 100Steps. 29 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2686 tokens

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 169: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 29 items
  • +4Reference files are cited in the instructions (5 of 5)
  • +3All 3 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.