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BC ai-interview

Multi-Domain Technical Interview Coach. Adaptive interview practice for AI/ML, Python, Java, Go, C, and other tech fields with performance tracking. Triggers on interview, mock interview, 面试, technical interview.

ClawHub Agent Skills author: zhanggroot7 v1.0.2 MIT-0 2 files body ≈ 4 046 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

ReferenceSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (ai-interview) differs from the folder (ai-interview-skill)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 60Failures and branches. 2 branches
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 4046 tokens
  • 100Steps. 35 steps
  • 100Progress reporting. Reports progress

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 212: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 35 items
  • +4Has examples (19 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: clean
This interview-coaching skill is legitimate, but it saves local profiles and full interview notes that users should understand before using it.
LLM: benign (high) · VirusTotal: · 29 May 2026