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CC verifica-fonti

Controlla coerenza e plausibilità delle citazioni normative italiane ed europee in un testo prodotto da un'altra skill legal-tech (o passato dall'avvocato). Verifica formato, esistenza apparente, coerenza interna, segnala citazioni sospette o non risolvibili. Usa quando una skill IT/EU ha appena prodotto un output con riferimenti normativi, o quando l'avvocato chiede esplicitamente "verifica le fonti", "controlla le citazioni", "queste citazioni reggono?".

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 2 files body ≈ 5 534 tokens Open the sourcegithub.com↗ analyzed 70 min ago

Controlla coerenza e plausibilità delle citazioni normative italiane ed europee in un testo prodotto da un'altra skill legal-tech (o passato dall'avvocato).

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

AnalyzerAI and agentsLegaltype 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
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
57/100
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. 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.
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: 2. 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 ≈ 5534 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 57/100

  • 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. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (verifica-fonti) differs from the folder (verifica-fonti-michele-loi)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 5534 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 33 steps
  • low 11 top-level sections: this looks like several domains in one skill

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)
  • -225 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 460: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 33 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.