Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dasein108/slope-studio --skill marketing-measure-learngit clone --depth 1 https://github.com/dasein108/slope-studioWrote 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/dasein108/slope-studio/marketing-measure-learn)<a href="https://agentmods.dev/skills/dasein108/slope-studio/marketing-measure-learn"><img src="https://agentmods.dev/badge/skills/dasein108/slope-studio/marketing-measure-learn/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/dasein108/slope-studio/marketing-measure-learn"><img src="https://agentmods.dev/badge/skills/dasein108/slope-studio/marketing-measure-learn.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 | $0.00104 | $0.01325 |
| Opus 5 | $0.00052 | $0.00662 |
| Sonnet 5 | $0.00021 | $0.00265 |
| Haiku 4.5 | $0.00010 | $0.00133 |
Grade A, and why
marketing-measure-learn 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 11d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
marketing-measure-learn — score, then steer
Measure and learn always run as a pair: first get the numbers (a deterministic API + math step), then reflect on them (agent judgement). Do them in order.
Step 1 — MEASURE (deterministic)
studio marketing measure --channel <name> --comments-n 60
Fetches views/likes/comments (+ retention & subs gained if the analytics scope is granted),
computes a virality composite (log-damped view-velocity + retention + engagement +
sub-conversion), ranks every video into a percentile within this channel's own portfolio,
and tags each win (≥P75) / loss (≤P25) / neutral / cold-start. Writes back to the
journal and drops 08_stats.json + 08_comments.json into each run dir.
Watch for:
- Wait for watch time — measuring same-day gives noise. 48–72h+ minimum.
- Cold-start (<10 deployed): percentiles are meaningless; every outcome is
cold-start. - Retention/subs need one extra OAuth scope; fetched best-effort, the loop runs fine
without. See
../marketing-guru/references/analytics.md. - Scoring weights (0.5 velocity / 0.2 retention / 0.2 engagement / 0.1 subs) are slated to
be re-tuned to a retention-first order per research finding F-SI9 — see
../marketing-guru/references/scoring.mdanddocs/20-research/self-improving-loop.md.
Step 1.5 — SNAPSHOT + SLICE (deterministic analysis)
Before changing strategy, collect age-bucket snapshots and ask the CLI for the hidden-relation pack. This is what lets the agent compare effects/cost/theme/music/sfx/animation at consistent ages instead of mixing a 1-day video with a 30-day video.
studio marketing due-snapshots --channel <name>
studio marketing snapshots --channel <name> --buckets 1,3,7,14,30
studio marketing insights --channel <name> --json
Use focused slices/comparisons when a pattern looks interesting:
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.
- 11d ago First seen · 103 lines · 104 tokens per session scan A ef03021c845e
marketing-measure-learn is a skill published in the GitHub repository dasein108/slope-studio (3 stars, last pushed 1mo ago), licensed MIT. It adds 104 tokens to every session and 1,325 once invoked, about $0.0005 per session on Opus 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 2026-08-31.
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