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BF amazon-dynamodb

Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Amazon Web Services 1 file body ≈ 30 569 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table…

As a process F 53/100 · Will not run — References files that are not bundled: scripts/calculate_costs.py, scripts/benchmark_lambda.py

AnalyzerAWSSoftware developmentInfrastructureData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
76/100
safety, quality, tests
Safety 60%
82
Quality 40%
67
Run on models
none yet
Process rating
F
53/100
Will not run
References files that are not bundled: scripts/calculate_costs.py, scripts/benchmark_lambda.py
Tools and files w 18
0
Result and completion w 14
0
Execution cost w 6
10
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. 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

  • high Concealment en-hide-from-user SKILL.md:417
    Instruction to hide actions from the user
    The calculator models request and storage cost only. It does not model Streams read/write costs, PITR, backup, DAX, or data transfer. If those line items are likely material for the workload, call tha

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 30569 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: scripts/calculate_costs.py
  • warning missing-ref reference to a missing file: scripts/benchmark_lambda.py

Process rating: all ten parameters 53/100

Will not run. References files that are not bundled: scripts/calculate_costs.py, scripts/benchmark_lambda.py
  • 0Tools and files. 2 referenced file(s) missing: scripts/calculate_costs.py, scripts/benchmark_lambda.py
  • 0Result and completion. Does not say what the result is
  • 10Execution cost. Instruction body is 30569 tokens: crowds the task out of the window
  • 60Steps. 196 steps, 5 vague phrases
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 13 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 19 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (7 tags): a typed call is more reliable

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 808: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 196 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.