Ahad690/growthkit-trends

GrowthKit Trends

A community, opt-in, federated dataset of public, anonymized short-form short-form-video trend and benchmark observations, contributed by users of the open-source GrowthKit Claude Code skill. It improves GrowthKit's default benchmarks over time so every founder starts from better, source-tagged ranges instead of fabricated numbers.

Honesty first. GrowthKit never lets a model invent a market metric. Numbers come from deterministic scripts run on a founder's own real exports. This dataset holds ONLY public, anonymized, aggregated observations — never owned analytics, handles, or per-post/per-install data.

What is (and isn't) here

Here: public trending-hashtag/sound observations from TikTok Creative Center, and aggregated performance benchmarks (e.g., the median of a metric across a contributor's posts in a category).

Never here (blocked by assert_public_only before any upload): raw CSV exports, account handles / names / usernames / profile URLs / emails, per-post identifiers (video_id, post_id) or per-post metrics (views, likes, comments, profile_visits), MMP/attribution exports, install-level rows, install_id, device_id, IP addresses, user_id.

Row schema

Each row is one JSON object with exactly these fields:

{
  "platform": "tiktok",
  "data_type": "hashtag_trend | sound_trend | perf_benchmark",
  "industry": "string",
  "country": "string",
  "metric_name": "string",
  "metric_value": 0,
  "period_days": 7,
  "captured_on": "YYYY-MM-DD",
  "source": "creative_center | aggregated_owned"
}

Field	Meaning
platform	Always tiktok in v1.
data_type	hashtag_trend, sound_trend, or perf_benchmark.
industry	Coarse industry/vertical label (e.g., saas, fitness).
country	ISO-style country code (e.g., US, GB).
metric_name	e.g. publish_cnt, video_views, completion_rate_median.
metric_value	Numeric. For perf_benchmark this is aggregated (e.g., a median), never per-post.
period_days	Observation window (default 7).
captured_on	Date the observation was captured (YYYY-MM-DD).
source	creative_center (public trend fetch) or aggregated_owned (anonymized aggregate of a contributor's own data).

How the data is stored — stack, don't rewrite

Each contribution is one new, content-addressed, append-only file at contributions/<author>-<sha256(payload)[:10]>.json (a JSON array of rows). Two contributors never collide on a path, resubmitting identical data is idempotent (same hash → same filename), and merging one PR can never clobber another. Because a Hugging Face repo is a git repo, every change is a commit with a SHA and any merge is one corrective commit from being reverted — consumers can also pin a known-good revision so a bad merge never reaches them.

Auto-merge (safe, unattended)

Clean PRs are merged by a bot (automerge.py, run on a daily GitHub Actions cron). A PR merges only if it clears every layer of the guard stack — additive-only (no removes/modifies; only new contributions/*.json), size cap, per-row schema/PII/range/enum validation, a corrupt-ratio gate (a single bad row holds the whole PR), and anti-abuse heuristics (flooding, group-median outliers). Anything that fails is commented and left open for a human, never silently dropped.

Honest boundary: these gates prove a row is well-formed, PII-free, in-range, non-duplicate, and statistically unremarkable. They do not prove the numbers are authentic — a patient adversary could submit plausible fake data. That residual risk is why versioning/revert matters: prevention narrows the blast radius; git versioning guarantees recovery.

License

CC-BY-4.0. You may share and adapt with attribution. (GrowthKit code is MIT; see the GitHub repo.)

How to contribute (via PR)

Contribution is off by default and runs locally with the GrowthKit skill:

# 1. Preview EXACTLY what would be shared — no upload happens:
python3 skills/growthkit/scripts/federation/contribute.py --rows rows.json --dry-run

# 2. To actually open a dataset PR, set a token and drop --dry-run:
export HF_TOKEN=hf_...      # contributors only; never shipped
python3 skills/growthkit/scripts/federation/contribute.py --rows rows.json

The contributor's machine strips each row to the schema above, runs assert_public_only (which aborts the entire contribution if any identifying or owned field is present), dedups, and opens a pull request to this dataset. Maintainers review PRs before merge. There is no background upload.

Pulling community data back into your local benchmarks:

python3 skills/growthkit/scripts/federation/refresh_dataset.py --dry-run

refresh_dataset.py validates every row (schema + range + banned-field check), refuses corrupt-heavy files, no-ops below a minimum new-row threshold, and labels community benchmarks with a coverage-aware confidence (LOW until a segment has enough rows).

See DATA_POLICY.md for the full policy.