SKILLEMALL.ai

AF zhihu-yanghao

This skill provides the full Zhihu account-nurturing (养号) workflow for a Zhihu account via ego-browser — user-configurable topic pool with vertical focus (创作垂直度), three-shift (morning/noon/evening) schedule where each shift writes+publishes+verifies one answer and runs like engagement, weekly deep-dive answers (深度版, user-invited or random), moments/想法 posting for follower intimacy, content-influence requirements (hook + actionable + CTA + comment reply), plus optional collect/follow/comment interactions driven by config. Use it when the user asks to 知乎养号 / 养知乎号 / 发知乎回答 / 知乎点赞互动 / 知乎浏览 / 三班养号 / 写深度版 / 发想法 / 提升创作分, or wants a portable, risk-controlled, topic-configurable Zhihu growth routine on any machine where ego-browser is installed.

ClawHub Agent Skills author: Evan Song v1.3.6 MIT-0 15 files body ≈ 3 231 tokens Open the sourceclawhub.ai analyzed 17 h ago

This skill provides the full Zhihu account-nurturing (养号) workflow for a Zhihu account via ego-browser — user-configurable topic pool with vertical focus…

As a process F 44/100 · Will not run — References files that are not bundled: scripts/verify_fold.js

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
F
44/100
Will not run
References files that are not bundled: scripts/verify_fold.js
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/verify_fold.js
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 44/100

Will not run. References files that are not bundled: scripts/verify_fold.js
  • 0Tools and files. 1 referenced file(s) missing: scripts/verify_fold.js
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 3 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 88 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3231 tokens
  • low 12 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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -224 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 745: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 88 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 7 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This skill is a disclosed Zhihu account-growth automation, but it disables sandboxing and can perform public account actions while using anti-abuse evasion tactics.
LLM: suspicious (high) · 12 Sept 2026