SKILLEMALL.ai

BC rapprochement-paiements

Étape ⑤ du pipeline comptable. Rapproche les opérations bancaires avec les factures et notes de frais classées, et rend compte au comptable de l'état des règlements par client. À utiliser dès que le comptable veut faire le point sur les paiements : « rapprochement », « lettrage », « où en sont les paiements ? », « qui n'a pas payé ? », « rapproche les relevés avec les factures », ou après que de nouvelles pièces ont été classées par le pipeline. Pour chaque facture : réglée / partielle / impayée / en retard. Signale les opérations à justifier (facture manquante / paiement orphelin), les relevés non lisibles et les paiements en double. Écrit, par client, les deux fichiers que lit le backend : company.json (identité) et rapprochement.json (suivi, format periods[]). Lecture seule sur l'arborescence des pièces : ne classe rien (classement-document) et ne déplace aucun fichier.

ClawHub Agent Skills author: trendex v1.0.0 MIT-0 5 files body ≈ 1 494 tokens Open the sourceclawhub.ai analyzed 26 h ago

Étape ⑤ du pipeline comptable.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
100
Quality 40%
62
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Étape ⑤ du pipeline comptable. Rapproche les opérations bancaires … ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 51/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1494 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 885: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -31 of 3 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (2 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
The skill’s accounting purpose is coherent, but it should be reviewed because it automatically creates persistent plaintext copies and caches of sensitive bank and invoice contents beyond the main disclosed outputs.
LLM: suspicious (high) · 4 Jun 2026