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FC WhatsApp Analyzer

Automatically detect appointments and urgent messages from WhatsApp, alert via Telegram, and optionally add to Google Calendar.

Not recommendedcritical or high security findings · low grade F
modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files · 1 script body ≈ 677 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Automatically detect appointments and urgent messages from WhatsApp, alert via Telegram, and optionally add to Google Calendar.

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

AnalyzerTelegramWhatsAppDockerSoftware developmentPersonal productivityAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
F
34/100
safety, quality, tests
Safety 60%
13
Quality 40%
65
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

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.

For the author

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.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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 · 12

  • high Dangerous commands cmd-persistence setup.sh:157
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    launchctl load "$PLIST"
  • high Dangerous commands cmd-persistence SKILL.md:70
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    launchctl unload ~/Library/LaunchAgents/ai.openclaw.whatsapp-store.plist
  • high Dangerous commands cmd-persistence SKILL.md:71
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    launchctl load ~/Library/LaunchAgents/ai.openclaw.whatsapp-store.plist
Medium and low: 9
  • medium Dangerous commands cmd-persistence setup.sh:125
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (string literal in code, not executed)
    PLIST="$HOME/Library/LaunchAgents/ai.openclaw.whatsapp-store.plist"
    code literal
  • medium Exfiltration net-credential-use setup.sh:224
    Credential used in a network call (verify the destination is the intended service)
    STATUS=$(curl -s -H "X-Api-Key: $WAHA_API_KEY" "http://localhost:$WAHA_PORT/api/sessions/default" 2>/dev/null | grep -o '"status":"[^"]*"' | cut -d'"' -f4)
  • medium Exfiltration net-credential-use setup.sh:227
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "X-Api-Key: $WAHA_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:64
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "X-Api-Key: $WAHA_API_KEY" http://loca…000/api/sessions/default | jq '.status'
  • medium Exfiltration net-credential-use SKILL.md:74
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "X-Api-Key: $WAHA_API_KEY" http://loca…000/api/default/auth/qr --output /tmp/qr.png
  • medium Exfiltration net-credential-use SKILL.md:84
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "X-Api-Key: $WAHA_API_KEY" http://loca…000/api/default/auth/qr --output /tmp/qr.png
  • low Dangerous commands cmd-background-process setup.sh:164
    Starts a background / autostarted process
    nohup $NODE_PATH "$WORK_DIR/message-store.js" > /tmp/whatsapp-store.log 2>&1 &
  • low Exfiltration read-dotenv SKILL.md:63
    Reads a .env file
    source ~/.openclaw/workspace/.whatsapp-messages/.env
  • low Exfiltration read-dotenv SKILL.md:83
    Reads a .env file
    source ~/.openclaw/workspace/.whatsapp-messages/.env

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 61/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (WhatsApp Analyzer) differs from the folder (whatsapp-telegram-calendar-alert)
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 10 steps
  • 100Execution cost. Instruction body is 677 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 127: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.