SKILLEMALL.ai

AC beijing-signed-price-tracker

Track configured Beijing Housing Commission new-home projects from bjjs.zjw.beijing.gov.cn project-detail URLs, read project signed-unit counts, signed area, and average price, crawl building tables including “查看更多” and paginated lists, treat both “已签约” and “网上联机备案” as signed units, estimate the implied average price per m² of newly signed rooms from changes between the previous and current project summaries, cache unsold room metadata locally, persist rows into a Feishu spreadsheet as the single source of truth, and send Feishu DM notifications after each run. Use when asked to monitor one or more Beijing pre-sale projects, update a project mapping, sync newly signed rooms into a Feishu sheet, infer newly signed average price, verify duplicate insertion behavior, or notify on updates.

ClawHub Agent Skills author: 张家钊 v1.6.0 MIT-0 8 files body ≈ 895 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

AnalyzerData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
81
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

    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 Secrets in code secret-high-entropy-token projects.json:8
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "appSecret": "nba3…7xm",
      quoted

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    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. 57 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 895 tokens
    • 100Running it twice. No mutating operations

    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
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 796: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 57 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: suspicious
    The tracker does what it says, but it ships with a real-looking Feishu app secret and default Feishu sheet/message destination that could be used as-is.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026