BD linkedin-monitor
Bulletproof LinkedIn inbox monitoring with progressive autonomy. Monitors messages hourly, drafts replies in your voice, and alerts you to new conversations. Supports 4 autonomy levels from monitor-only to full autonomous.
As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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 · 4
✓ No critical or high findings
Medium and low: 4
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low Dangerous commands
cmd-cron-mentiondocs/SETUP.md:91Mentions editing / listing crontabcrontab -l | grep linkedin
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low Dangerous commands
cmd-cron-mentiondocs/TROUBLESHOOT.md:126Mentions editing / listing crontab (detector / deny-list definition)3. Check cron is installed: `crontab -l | grep linkedin`
detector -
low Dangerous commands
cmd-cron-mentionREADME.md:271Mentions editing / listing crontab# Check: crontab -l
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low Dangerous commands
cmd-cron-mentionscripts/health.sh:137Mentions editing / listing crontabif crontab -l 2>/dev/null | grep -q "linkedin-monitor"; then
Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 48/100
- 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. 1 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 100Steps. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 928 tokens
- low 10 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)
- +3Output format is not stated: the model decides each time
- -42 reference files, but SKILL.md never points to them: the model will not open them
- -37 of 7 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 222: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.
External checks
ClawHub: suspicious
The skill is a coherent LinkedIn inbox monitor, but it asks for persistent access to a logged-in LinkedIn account and external messaging channels with under-scoped safeguards for credentials, automatic actions, and untrusted message content.
LLM: suspicious (high) · VirusTotal: suspicious · 10 Sept 2026