BD 微信AI接入顾问
微信小程序AI怎么接，先别急着写代码 开发模式/自动模式怎么选，商品服务怎么结构化，一次讲清 说你的业务就能出接入方案和代码骨架
微信小程序AI怎么接，先别急着写代码 开发模式/自动模式怎么选，商品服务怎么结构化，一次讲清 说你的业务就能出接入方案和代码骨架
As a process D 49/100 · Unfinished process — 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.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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-dumpSOUL.mdAgent memory / workspace files bundled with the skill (1) — likely a workspace dump with personal data or tokensSOUL.md
Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-long-hermesdescription is 64 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "platform" - note
frontmatter-keyunknown frontmatter key "requires_multi_agent" - note
frontmatter-keyunknown frontmatter key "runtime_requires" - note
frontmatter-keyunknown frontmatter key "skill_requires" - note
frontmatter-keyunknown frontmatter key "install_check"
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
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (微信AI接入顾问) differs from the folder (wechat-aiagent-dev)
- 100Tools and files. No external tools needed
- 100Steps. 35 steps
- 100Execution cost. Instruction body is 762 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)
- +3Description length 64: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +4Structure: 7 headings
- +3Step-by-step instructions: 35 items
- +4Reference files are cited in the instructions (6 of 7)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.