Borrowing it
Nothing to install: this file belongs to umsachde/re-com. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/umsachde/re-com/main/.claude/skills/recom-quality-check/SKILL.mdgit clone --depth 1 https://github.com/umsachde/re-comWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/umsachde/re-com/recom-quality-check)<a href="https://agentmods.dev/skills/umsachde/re-com/recom-quality-check"><img src="https://agentmods.dev/badge/skills/umsachde/re-com/recom-quality-check/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/umsachde/re-com/recom-quality-check"><img src="https://agentmods.dev/badge/skills/umsachde/re-com/recom-quality-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.00888 |
| Opus 5.5 | $0.00022 | $0.00355 |
| Sonnet 5 | $0.00011 | $0.00178 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
recom-quality-check scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scripts/quality_check.py is the third verification layer (PLAN.md §5) —
it turns ranking-quality questions into numbers instead of impressions. Run it
by hand whenever a change touches ranking, signal weighting, mood scoring, or
the graph.
Commands
python scripts/quality_check.py --titles # mood/arc cases, human-readable output
python scripts/quality_check.py --distinctiveness 0 # A/B the seed-scoring logic
python scripts/quality_check.py --similarity --repeat # scores recommend_from_song / recommend_from_playlist, plus a noise floor in the same run
Always prefer --repeat when comparing two versions of the ranking logic — it
measures the noise floor (run-to-run variance) in the same invocation, so a
delta can be judged against it instead of against a stale number from
PLAN.md.
The metrics, and what they actually mean
- Mean mood fit — average fit score. Do not stop here.
PLAN.md§3 records a build that scored a healthy 0.775 mean fit while returning 70% the same songs for "heartbroken" and "angry" — fit alone cannot see that kind of collapse. - Cross-mood overlap (lower is better) — the metric that does catch that failure mode. Always check this alongside mean fit, never instead of it.
- Cross-seed overlap (lower is better,
--similarityonly) — the same-shape check for the similarity path: whether many different seeds funnel into one popular attractor. - Signal agreement / corroborated (
--similarity) — reported against a per-backend ceiling, not as a raw count. YouTube's ceiling is higher than Spotify's because Spotify has fewer native signals (capabilities()is emptier there) — a bare cross-backend mean would misread that difference as a regression. - Native-vs-graph A/B: churn vs. corroboration delta — churn alone cannot
answer "did this help or dilute", because both arms truncate to the same
limitand can show identical churn/additions by pure arithmetic (PLAN.md§6.5 — this happened, 37 == 37). The corroboration delta is the number that actually answers helping-vs-diluting. - Artist concentration (HHI) — measure it before any
max_per_artistcap is applied; measuring after only confirms the cap works, it doesn't tell you anything about the underlying ranking.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 69 lines · 56 tokens per session scan A 459ef489fa33
recom-quality-check is a skill published in the GitHub repository umsachde/re-com (0 stars, last pushed 2d ago), licensed MIT. It adds 56 tokens to every session and 888 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-14.
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