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BF data-type-classifier

Classify construction data by type (structured, unstructured, semi-structured). Analyze data sources and recommend appropriate storage/processing methods

ClawHub Agent Skills author: datadrivenconstruction v2.1.0 4 files body ≈ 6 117 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process F 34/100 · Will not run — References files that are not bundled: ../sql-query-builder/SKILL.md, ../../Chapter-2.4/pdf-to-structured/SKILL.md, ../../Chapter-2.5/data-model-designer/SKILL.md

AnalyzerInfrastructuretype 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
80/100
safety, quality, tests
Safety 60%
100
Quality 40%
49
Run on models
none yet
Process rating
F
34/100
Will not run
References files that are not bundled: ../sql-query-builder/SKILL.md, ../../Chapter-2.4/pdf-to-structured/SKILL.md, ../../Chapter-2.5/data-model-designer/SKILL.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  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 · 0

✓ No critical or high findings

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 6117 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: ../sql-query-builder/SKILL.md
  • warning missing-ref reference to a missing file: ../../Chapter-2.4/pdf-to-structured/SKILL.md
  • warning missing-ref reference to a missing file: ../../Chapter-2.5/data-model-designer/SKILL.md
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 34/100

Will not run. References files that are not bundled: ../sql-query-builder/SKILL.md, ../../Chapter-2.4/pdf-to-structured/SKILL.md, ../../Chapter-2.5/data-model-designer/SKILL.md
  • 0Tools and files. 3 referenced file(s) missing: ../sql-query-builder/SKILL.md, ../../Chapter-2.4/pdf-to-structured/SKILL.md, ../../Chapter-2.5/data-model-designer/SKILL.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 70Execution cost. Instruction body is 6117 tokens
  • 100Steps. 5 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations

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 153: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 49.

External checks

ClawHub: clean
This skill is a construction data classifier that uses user-provided data or file paths to recommend data formats, processing tools, and storage approaches.
LLM: benign (high) · VirusTotal: benign · 10 Sept 2026