SKILLEMALL.ai

BC prompt-engineer

A comprehensive prompt engineering skill for AI developers building LLM-powered applications. Covers system prompt design, few-shot prompting, chain-of-thought reasoning, tool-use patterns, structured output enforcement, context management, prompt templates, evaluation frameworks, multi-turn conversation handling, multimodal prompting, agent patterns such as ReAct and plan-and-execute, and safety guardrails including prompt injection defence.

ClawHub Agent Skills author: royhk920 v1.0.3 MIT-0 5 files · 1 script body ≈ 10 285 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 61/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

GeneratorAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
98
Quality 40%
75
Run on models
none yet
Process rating
C
61/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Exfiltration read-dotenv scripts/scaffold.sh:120
    Reads a .env file (quoted — discussed, not commanded)
    echo "  cp .env.example .env  # Add your API key"
    quoted

A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 10285 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 61/100

  • 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. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (prompt-engineer) differs from the folder (ai-prompt-engineer)
  • 40Execution cost. Instruction body is 10285 tokens: crowds the task out of the window
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 45 steps
  • 100Failures and branches. 3 branches, has a failure section
  • low 16 top-level sections: this looks like several domains in one skill
  • low The skill ranks results itself: that belongs to the system behind the tool, not the model

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)
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 446: enough signal without eating the budget
  • +4Structure: 73 headings
  • +3Step-by-step instructions: 45 items
  • +3Output format is stated explicitly
  • +4Has examples (59 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This skill is a prompt-engineering reference with user-run examples that use an OpenAI API key and send evaluation prompts to OpenAI when executed.
LLM: benign (high) · VirusTotal: · 29 May 2026