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CD kaggle-learner

This skill should be used when the user asks to "learn from Kaggle", "study Kaggle solutions", "analyze Kaggle competitions", or mentions Kaggle competition URLs. Provides access to extracted knowledge from winning Kaggle solutions across NLP, CV, time series, tabular, and multimodal domains.

Galaxy-Dawn/claude-scholar Agent Skills author: Galaxy-Dawn MIT 20 files body ≈ 852 tokens Open the sourcegithub.com↗ analyzed 2 d ago

This skill should be used when the user asks to "learn from Kaggle", "study Kaggle solutions", "analyze Kaggle competitions", or mentions Kaggle competition…

As a process D 39/100 · Unfinished process — References files that are not bundled: references/knowledge/cv/, references/knowledge/multimodal/, references/knowledge/[domain]/

AnalyzerPeople and hiringSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
85/100
safety, quality, tests
Safety 60%
90
Quality 40%
77
Run on models
none yet
Process rating
D
39/100
Unfinished process
References files that are not bundled: references/knowledge/cv/, references/knowledge/multimodal/, references/knowledge/[domain]/
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

The same skill appears in 1 more place: RA-Skills

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 · 10

✓ No critical or high findings

Medium and low: 10
  • low Secrets in code secret-high-entropy-token references/knowledge/.archive/nlp.md:624
    High-entropy token-like string (may be an id, hash or a credential)
    | **2nd** | imagination-research | ~30/50 | SFT + DPO (长度优化), 代码执行 | Deep…14B | lmdeploy |
  • low Secrets in code secret-high-entropy-token references/knowledge/.archive/nlp.md:625
    High-entropy token-like string (may be an id, hash or a credential)
    | **3rd** | Aliev | ~29/50 | Self-consistency, Early stopping | Deep…14B AWQ | vLLM |
  • low Secrets in code secret-high-entropy-token references/knowledge/.archive/nlp.md:626
    High-entropy token-like string (may be an id, hash or a credential)
    | **4th** | Soren Ravn Andersen | ~28/50 | AWQ 量化, Self-consistency | Deep…14B AWQ | vLLM |
  • low Secrets in code secret-high-entropy-token references/knowledge/.archive/nlp.md:627
    High-entropy token-like string (may be an id, hash or a credential)
    | **5th** | usernam | ~27/50 | lmdeploy 高吞吐量 | Deep…14B AWQ | lmdeploy |
  • low Secrets in code secret-high-entropy-token references/knowledge/.archive/nlp.md:628
    High-entropy token-like string (may be an id, hash or a credential)
    | **7th** | tascj | ~26/50 | AWQ 量化 | Deep…14B AWQ | lmdeploy |
  • low Secrets in code secret-high-entropy-token references/knowledge/nlp/aimo-2-2025.md:66
    High-entropy token-like string (may be an id, hash or a credential)
    | **2nd** | imagination-research | ~30/50 | SFT + DPO (长度优化), 代码执行 | Deep…14B | lmdeploy |
  • low Secrets in code secret-high-entropy-token references/knowledge/nlp/aimo-2-2025.md:67
    High-entropy token-like string (may be an id, hash or a credential)
    | **3rd** | Aliev | ~29/50 | Self-consistency, Early stopping | Deep…14B AWQ | vLLM |
  • low Secrets in code secret-high-entropy-token references/knowledge/nlp/aimo-2-2025.md:68
    High-entropy token-like string (may be an id, hash or a credential)
    | **4th** | Soren Ravn Andersen | ~28/50 | AWQ 量化, Self-consistency | Deep…14B AWQ | vLLM |
  • low Secrets in code secret-high-entropy-token references/knowledge/nlp/aimo-2-2025.md:69
    High-entropy token-like string (may be an id, hash or a credential)
    | **5th** | usernam | ~27/50 | lmdeploy 高吞吐量 | Deep…14B AWQ | lmdeploy |
  • low Secrets in code secret-high-entropy-token references/knowledge/nlp/aimo-2-2025.md:70
    High-entropy token-like string (may be an id, hash or a credential)
    | **7th** | tascj | ~26/50 | AWQ 量化 | Deep…14B AWQ | lmdeploy |

Files scanned: 20. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/knowledge/cv/
  • warning missing-ref reference to a missing file: references/knowledge/multimodal/
  • warning missing-ref reference to a missing file: references/knowledge/[domain]/

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: references/knowledge/cv/, references/knowledge/multimodal/, references/knowledge/[domain]/
  • 0Tools and files. 3 referenced file(s) missing: references/knowledge/cv/, references/knowledge/multimodal/, references/knowledge/[domain]/
  • 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
  • 70When it triggers. States when to use, but not when not to
  • 85Steps. 27 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 852 tokens
  • 100Running it twice. No mutating operations

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

  • +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
  • +5Description quotes 3 example trigger phrases
  • +3Description length 293: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 27 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.