SKILLEMALL.ai

BB backend-engineer

Backend, API, infrastructure, cloud, integration, networking, DevOps, deployment, and Terraform engineering. Use when the user asks to build, design, debug, deploy, integrate, or optimize backend systems, APIs, microservices, cloud infrastructure, CI/CD pipelines, databases, or networks. Covers REST, GraphQL, gRPC, message brokers, container orchestration, IaC (Terraform/CloudFormation), observability, performance, payments, IP/copyright, and developer experience.

ClawHub Agent Skills author: Jackskill v1.0.2 MIT-0 2 files body ≈ 16 866 tokens Open the sourceclawhub.ai analyzed 2 d ago

Backend, API, infrastructure, cloud, integration, networking, DevOps, deployment, and Terraform engineering.

As a process B 68/100 · Nearly there — weak spots: result and completion, execution cost

IntegrationTerraformAWSGoogle CloudAzureInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
Execution cost w 6
10
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 0

✓ No critical or high findings

Files scanned: 0. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 16866 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "triggers"

Process rating: all ten parameters 68/100

  • 0Result and completion. Does not say what the result is
  • 10Execution cost. Instruction body is 16866 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, web, git, python, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 429 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 1 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 The response is described with custom markup (9 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

  • +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
  • +1No license
  • +2Single-language instructions
  • +3Description length 468: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 429 items
  • +4Has examples (11 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: clean
This is a broad backend engineering guidance skill with some overbroad activation terms, but it does not install code, persist, hide behavior, or direct unrelated data access.
LLM: benign (high) · VirusTotal: · 21 Jul 2026