SKILLEMALL.ai

BB secondme-dev-assistant

Use when user wants to develop on the SecondMe platform (second.me, develop.second.me). Triggers: building SecondMe third-party apps (第三方应用/外部应用), SecondMe OAuth login integration (Client ID/Secret, token exchange), MCP integration for SecondMe, Agent Memory API, Act stream API, app scaffolding, review submission, or hackathon/黑客松 projects targeting SecondMe. Covers the full developer lifecycle from app creation and credentials to release. NOT for casual SecondMe usage like browsing profiles, adding friends, or social features — only for building and integrating with SecondMe as a developer platform.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: Mindverse v2.1.0 MIT-0 10 files body ≈ 2 567 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 72/100 · Nearly there — weak spots: inputs and preconditions, running it twice

IntegrationAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
82
Quality 40%
96
Run on models
none yet
Process rating
B
72/100
Nearly there
Inputs and preconditions w 11
0
Running it twice w 4
30
When it triggers w 12
50
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.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

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.
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

  • high Concealment en-hide-from-user SKILL.md:15
    Instruction to hide actions from the user
    On first activation per conversation, silently run this check before proceeding with the user's request:

Files scanned: 10. 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 72/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 8 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Result and completion. Output format stated, no completion criterion
  • 70Failures and branches. 13 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 69 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2567 tokens
  • 100Progress reporting. Reports progress
  • low 15 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
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 607: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 69 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (8 of 8)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

External checks

ClawHub: suspicious
This appears to be a real SecondMe developer helper, but it silently self-updates and stores local telemetry and credentials, so it needs review before use.
LLM: suspicious (high) · VirusTotal: · 29 May 2026