BC nex-gdpr
GDPR and AVG (Belgian data protection law) compliance handler for agency operators, data controllers, and organizations managing data subject requests. Register and manage all types of data subject requests (inzageverzoek, verwijderverzoek, recht op gegevensoverdracht) as required under GDPR Articles 15-21 and Belgian AVG regulations. Automatically scan and discover personal data across OpenClaw sessions, agent memory, application logs, and skill databases. Process Right of Access requests by compiling complete personal data exports in machine-readable formats. Handle Right of Erasure requests with secure 3-pass file deletion and audit logging. Support Right to Data Portability with JSON format exports. Generate compliant response letters in both Dutch (AVG) and English (GDPR) with formal documentation. Track 30-day legal response deadlines with extension options for complex requests. Maintain immutable audit trails of every action taken on data subject requests for regulatory compliance and dispute resolution. Manage data retention policies and auto-cleanup schedules. Perfect for Belgian agencies, service providers, and organizations operating under GDPR/AVG who need systematic compliance processes.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- Shorten the description to 1024 characters.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Dangerous commands
cmd-shell-rcsetup.sh:132Writes to a shell startup fileecho "Add this to your shell profile (~/.bashrc, ~/.zshrc, etc.):"
Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1219 chars, limit 1024
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 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. 9 mutating operations with no state check
- 55Failures and branches. 1 branches
- 100Tools and files. No external tools needed
- 100Steps. 75 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1956 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1219: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +4Structure: 21 headings
- +3Step-by-step instructions: 75 items
- +4Has examples (19 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.