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Self-hosted aircraft monitor — watches one or more named locations for interesting aircraft (military/gov/police via plane-alert-db CSVs, emergency squawks 7500/7600/7700, custom ICAO hex/type lists, low-flyers by altitude, or literally everything) using free online ADS-B APIs (adsb.lol, adsb.fi, airplanes.live, adsb.one) or your own ultrafeeder — no SDR, no antenna, no hardware. Fires alerts to Telegram (with doc8643 aircraft-type photos) and/or webhooks (JSON array + base64 image), per-alert cooldowns, config-file driven (config.yaml), runs in Docker. Use when the user wants to monitor/alert on aircraft near a location, get pinged when a military/government/police plane flies overhead, watch for emergency squawks, or build their own plane-spotting radar without buying hardware.

ClawHub Agent Skills author: Ciprian Mandache v1.8.6 MIT-0 3 files body ≈ 2 003 tokens Open the sourceclawhub.ai analyzed 2 d ago

Self-hosted aircraft monitor — watches one or more named locations for interesting aircraft (military/gov/police via plane-alert-db CSVs, emergency squawks…

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationTelegramDockerDiscordData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
86
Run on models
none yet
Process rating
D
49/100
Unfinished process
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

    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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration exfil-webhook-url references/setup.md:296
      Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)
      2. **Personal alerts:** send your bot a message first, then check `https://api.telegram.org/bot<TOKEN>/getUpdates` — your `chat_id` is in there.
      placeholder

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "permissions"

    Process rating: all ten parameters 49/100

    • 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
    • 30Running it twice. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 14 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2003 tokens
    • 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 790: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 14 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: clean
    This skill is a clearly disclosed aircraft-alerting Docker workflow that sends configured location and aircraft data to user-selected ADS-B, Telegram, or webhook services.
    LLM: benign (high) · VirusTotal: · 1 Aug 2026