BC realestate-advisor
AI 房产资产顾问（置安居）。面向业主/买房者/置换者的房产资产理解与决策辅助工具。 触发词：房产估值、小区房价、房子值多少钱、成交记录、挂牌竞争、市场行情、买卖决策、房产置换。 支持三大模式：业主模式（估值/成交/挂牌/市场/决策）、买房者模式（负担能力/房源评估/报价策略）、置换模式（先卖后买 vs 先买后卖）。 数据说话，区间优于点值，置信度透明，利好利空并列不偏不倚。
AI 房产资产顾问（置安居）。面向业主/买房者/置换者的房产资产理解与决策辅助工具。 触发词：房产估值、小区房价、房子值多少钱、成交记录、挂牌竞争、市场行情、买卖决策、房产置换。 支持三大模式：业主模式（估值/成交/挂牌/市场/决策）、买房者模式（负担能力/房源评估/报价策略）、置换模式（先卖后买 vs…
As a process C 53/100 · 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 asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 Broad scope
meta-agent-memory-dumpHEARTBEAT.mdAgent memory / workspace files bundled with the skill (6) — likely a workspace dump with personal data or tokensHEARTBEAT.md, IDENTITY.md, MEMORY.md, SOUL.md, USER.md
Files scanned: 10. 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 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. 44 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 665 tokens
- 100Running it twice. No mutating operations
- 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
- -218 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 189: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 44 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.