Kaggle Solution Write-up Writer
Draft, structure, and review Kaggle solution write-ups with a section contract distilled from 5,700+ public write-ups.
$5
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Example session with this skill installed
Draft my 5th place solution write-up for BirdCLEF 2024. Notes: EfficientNet-B0 backbone, 5-fold CV, pseudo-labeling on the unlabeled soundscapes gave +0.012 CV. Final CV 0.81, private LB 0.82.
- Read your context and instructions
- Compiled the kaggle solution write-up
Summary
I drafted the write-up from your notes using only the numbers you gave me. Several things are missing, and I marked them rather than guess. Answer the questions at the end and I will fill them in.
5th Place Solution
Thanks to the hosts for another well-run BirdCLEF, and congratulations to everyone who took part. [Credit by @handle: which public notebooks or discussion posts did you build on?]
TL;DR
A single-architecture solution: EfficientNet-B0 trained with 5-fold cross-validation, plus pseudo-labeling on the unlabeled soundscapes. Pseudo-labeling was the change that mattered most, worth +0.012 CV. Final scores: CV 0.81, public LB not reported, private LB 0.82.
Data & preprocessing
[Needed: sample rate, spectrogram parameters, chunk length, and whether you used external data. A reproducer has to match these exactly.]
Validation
5-fold cross-validation.
[Needed: how the folds were split, and why.] Final CV was 0.81 against a private LB of 0.82. Public LB was not reported, so I make no claim about how closely CV tracked the leaderboard during the competition.
Model
EfficientNet-B0. [Needed: anything you changed from the off-the-shelf version.]
Training
[Needed: loss, schedule, epochs, and the hyperparameters that mattered.]
Pseudo-labeling
Pseudo-labels generated on the unlabeled soundscapes and added to training: +0.012 CV.
Results
| Change | CV | Public LB | Private LB |
|---|---|---|---|
| Pseudo-labeling on unlabeled soundscapes | +0.012 | not measured | not measured |
| Final submission | 0.81 | not measured | 0.82 |
There is no baseline row. You did not report a pre-pseudo-labeling score, and subtracting the delta from the final CV would present a number nobody measured.
What didn't work
[Needed: each dead end and why it failed. This section is required, and I will not invent experiments.]
Code & artifacts
[Needed: absolute links to the repo, inference notebook, dataset, and model weights.]
Before you post
- What was your public LB score?
- Did you measure a score before pseudo-labeling? If so I will add a baseline row.
- What did you try that failed, and why do you think it failed?
- Whose public work did you build on? I need @handles and links.
- BirdCLEF caps inference runtime. What was your measured runtime against the cap?
The draft is about 300 words. Once the gaps are filled it should land near the 700 to 900 words the most-upvoted write-ups run to, and a pipeline diagram would help.
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About this skill
Turn your experiment notes into a Kaggle solution write-up people actually read. This skill is not a style opinion: it was distilled from every public solution write-up linked from a Kaggle leaderboard (5,700+ write-ups across 540+ competitions) by measuring what the most-upvoted posts contain that typical posts do not, and turning those differences into a section contract your agent follows.
What it does
Ask for a write-up in your own words and the skill triggers on its own. It gathers the competition's facts and the other teams' write-ups, asks you for any score deltas you left out, and produces a draft that follows the contract. It also reviews and revises drafts you already have.
What it enforces
- A section contract. The sections real top write-ups use, in the order they actually use them (measured median ordering, not invented).
- Numbers on every claim. CV, public LB, and private LB stated explicitly; a delta attached to each technique, or the technique marked unmeasured.
- No invented scores. The most common failure when an agent drafts from incomplete notes is back-computing a baseline from final minus delta and presenting it as a measurement. The results-table row schema blocks this; unmeasured cells read
not measured. - A real "what didn't work" section. With reasons, so a reader can skip or retry each dead end.
- Credit and artifact links. @-handles for the public kernels and discussions you built on, absolute links to code, notebook, dataset, and model.
It also knows Kaggle's own pre-filled write-up template (Context / Overview of the Approach / Details of the submission / Sources) and when to use it instead, such as prize-eligible winners' documentation.
What's inside
SKILL.md: the contract, the rules, and a pre-posting checklist.references/corpus-evidence.md: the measurements, cut by finishing position and by community votes. Every recommendation traces to a row in it.references/exemplars.md: worked examples of results tables, "what didn't work" sections, and openings from real write-ups, with links to the originals.
Who it's for
Kaggle competitors posting an "Nth place solution", filling in the write-up form linked from a leaderboard, or submitting winners' documentation for prize eligibility.
Good to know
The skill works on its own from your notes. If the nvidia-kaggle skill is installed, it uses that skill's fetch scripts to pull competition facts and other teams' write-ups automatically.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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