SKILLEMALL.ai
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BB do-in-steps

Execute one complex task as ordered, dependent steps run sequentially, passing context from each step to the next, with per-step LLM-as-a-judge verification. Use when later steps depend on the results of earlier ones.

NeoLabHQ/context-engineering-kit Agent Skills author: NeoLabHQ 1 file body ≈ 18 182 tokens Open the sourcegithub.com↗ analyzed 4 d ago

Execute one complex task as ordered, dependent steps run sequentially, passing context from each step to the next, with per-step LLM-as-a-judge verification.

As a process B 67/100 · Nearly there — weak spots: inputs and preconditions, execution cost, running it twice

ProcedureWriting and documentsAI and agentsData and analyticstype 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
B
67/100
Nearly there
Progress reporting w 2
0
Execution cost w 6
10
Inputs and preconditions w 11
30
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: do-in-steps (NeoLabHQ/context-engineering-kit)

The same skill appears in 1 more place: context-engineering-kit

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

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 18182 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 1334, 1340): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 67/100

  • 0Progress reporting. Says nothing while it works
  • 10Execution cost. Instruction body is 18182 tokens: crowds the task out of the window
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 9 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 85Steps. 161 steps, 3 vague phrases
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 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 (4 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 217: enough signal without eating the budget
  • +4Structure: 39 headings
  • +3Step-by-step instructions: 161 items
  • +3Output format is stated explicitly
  • +4Has examples (33 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.