SKILLEMALL.ai

AC e-facture-rapprochement

Ingère les FACTURES (Factur-X / UBL / CII en fast-path, et surtout les PHOTOS DE TICKETS DE CAISSE et PDF par extraction LLM avec score de confiance) ET les RELEVÉS BANCAIRES (CAMT.053 / OFX / CSV), puis RAPPROCHE paiements ↔ factures et écrit company.json + rapprochement.json au contrat Pocket-Claw. Déclenche ce skill dès qu'il s'agit de rapprochement bancaire, de lettrage, de matcher des paiements avec des factures, d'ingérer des tickets/factures/relevés pour la compta, de produire rapprochement.json, ou de repérer les factures impayées / paiements orphelins / opérations injustifiées d'un client — même si l'utilisateur ne dit pas explicitement « rapprochement ». Le vrai travail est fait par scripts/main.py ; ne réimplémente jamais le moteur à la main et n'écris jamais le JSON à la main.

ClawHub Agent Skills author: trendex v1.0.0 MIT-0 20 files body ≈ 1 404 tokens Open the sourceclawhub.ai analyzed 28 h ago

Ingère les FACTURES (Factur-X / UBL / CII en fast-path, et surtout les PHOTOS DE TICKETS DE CAISSE et PDF par extraction LLM avec score de confiance) ET les…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticsAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
53/100
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: 20. 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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 13 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1404 tokens
  • 100Running it twice. No mutating operations
  • low The response is described with custom markup (3 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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 799: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 13 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 7 scripts are documented
  • +1License stated

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

External checks

ClawHub: clean
This skill coherently reconciles invoices and bank statements, but users should run it only on intended accounting folders because it handles sensitive financial records.
LLM: benign (medium) · VirusTotal: · 4 Jun 2026