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BD AI RecSys Weekly Report

自动生成关于"AI大模型（LLM、VLM等）技术在搜索、广告、推荐（搜广推）领域应用"的深度技术周报， 并自动同步到 IMA（腾讯文档/知识库）。触发词：AI搜广推技术周报、搜广推周报、大模型推荐报告、 推荐系统技术周报、生成式推荐周报、Transformer推荐系统报告、Scaling Law推荐系统、 MoE推荐系统、稀疏注意力推荐、RankMixer、OneTrans、MixFormer、BlossomRec。 当用户要求定期生成搜广推领域的技术研究报告、论文综述或行业动态分析时触发此 skill。

ClawHub Agent Skills author: fandywang87 v1.0.0 MIT-0 5 files body ≈ 1 019 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorData and analyticsAI and agentsInfrastructuretype 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
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
Run on models
none yet
Process rating
D
39/100
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

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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "triggers"

Process rating: all ten parameters 39/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (AI RecSys Weekly Report) differs from the folder (ai-recsys-weekly-report)
  • 60Tools and files. Uses tools (web, python, node) that frontmatter does not declare
  • 100Steps. 46 steps
  • 100Execution cost. Instruction body is 1019 tokens

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
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +2Single-language instructions
  • +3Description length 255: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 46 items
  • +4Has examples (4 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: clean
This skill openly generates AI recommendation-system reports and syncs them to Tencent IMA, but users should handle the IMA API key carefully.
LLM: benign (high) · VirusTotal: · 29 May 2026