SKILLEMALL.ai
GitHub ↗ RU Sign in

BC raisonnement-juridique

Modélise le raisonnement juridique d'un magistrat français pour l'analyse de dossiers civils. Utiliser ce skill pour analyser un litige et identifier les questions juridiques, construire un raisonnement juridique structuré (syllogisme), rédiger une motivation de jugement civil, rechercher la jurisprudence et les textes applicables via Judilibre et Légifrance, qualifier juridiquement des faits et actes, distinguer prétentions, moyens et arguments. Basé sur les Fiches méthodologiques de rédaction du jugement civil (ENM/Cour de cassation, 2023).

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 8 files body ≈ 3 150 tokens Open the sourcegithub.com↗ analyzed 85 min ago

Modélise le raisonnement juridique d'un magistrat français pour l'analyse de dossiers civils.

As a process C 54/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerSoftware developmentWriting and documentsAI and agentstype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
54/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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.
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: 8. 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")

Process rating: all ten parameters 54/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (raisonnement-juridique) differs from the folder (raisonnement-juridique-amaury-fouret)
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 84 steps
  • 100Execution cost. Instruction body is 3150 tokens

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)
  • +2Single-language instructions
  • +3Description length 548: enough signal without eating the budget
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 84 items
  • +3Output format is stated explicitly
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.