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BD rebate-assistant

返利宝统一技能。只按 3 个用户场景工作：S01 授权与教程、S02 链接返利、S03 商品搜索。用户说“返利”“教程”“详细教程”“提现教程”“提现10元”“确认提现”“我已授权”“账户余额”等走 S01；发送淘宝、京东、拼多多商品链接走 S02；表达想买什么商品时走 S03。S03 的职责是提取商品搜索信息，调用搜索接口，并返回后续可生成返利链接的商品结果。

ClawHub Agent Skills author: wuweizhen v1.0.1 MIT-0 26 files body ≈ 1 364 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructuretype 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
85/100
safety, quality, tests
Safety 60%
95
Quality 40%
69
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

The same skill appears in 3 more places: ClawHub, ClawHub, ClawHub

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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Secrets in code secret-high-entropy-token scripts/cli/recognize_fuzzy_product_search.js:4
    High-entropy token-like string (may be an id, hash or a credential)
    const reco…h_1 = require("../recognizeFuzzyProductSearch");
  • low Secrets in code secret-high-entropy-token scripts/cli/recognize_precise_product_search.js:4
    High-entropy token-like string (may be an id, hash or a credential)
    const reco…h_1 = require("../recognizePreciseProductSearch");
  • low Secrets in code secret-high-entropy-token scripts/productSearch.js:10
    High-entropy token-like string (may be an id, hash or a credential)
    const reco…h_1 = require("./recognizePreciseProductSearch");
  • low Secrets in code secret-high-entropy-token scripts/rebateAssistantRouter.js:10
    High-entropy token-like string (may be an id, hash or a credential)
    const reco…h_1 = require("./recognizePreciseProductSearch");
  • low Secrets in code secret-high-entropy-token scripts/recognizeFuzzyProductSearch.js:7
    High-entropy token-like string (may be an id, hash or a credential)
    const reco…h_1 = require("./recognizePreciseProductSearch");

Files scanned: 26. 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 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 (rebate-assistant) differs from the folder (jd-rebate)
  • 100Tools and files. No external tools needed
  • 100Steps. 118 steps
  • 100Execution cost. Instruction body is 1364 tokens
  • 100Running it twice. No mutating operations
  • low 11 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
  • -312 of 12 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 182: enough signal without eating the budget
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 118 items
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This is a coherent rebate assistant, but it handles account-linked rebate data and confirmed withdrawal requests, so users should understand those flows before installing.
LLM: benign (medium) · VirusTotal: · 29 May 2026