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 jscraik/Agent-Skills --skill talk-dubnov-merge-rate-ai-adoptiongit clone --depth 1 https://github.com/jscraik/Agent-SkillsWrote 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/jscraik/agent-skills/talk-dubnov-merge-rate-ai-adoption)<a href="https://agentmods.dev/skills/jscraik/agent-skills/talk-dubnov-merge-rate-ai-adoption"><img src="https://agentmods.dev/badge/skills/jscraik/agent-skills/talk-dubnov-merge-rate-ai-adoption/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/jscraik/agent-skills/talk-dubnov-merge-rate-ai-adoption"><img src="https://agentmods.dev/badge/skills/jscraik/agent-skills/talk-dubnov-merge-rate-ai-adoption.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 | $0.00139 | $0.00997 |
| Opus 5 | $0.00069 | $0.00498 |
| Sonnet 5 | $0.00028 | $0.00199 |
| Haiku 4.5 | $0.00014 | $0.00100 |
Grade A, and why
talk-dubnov-merge-rate-ai-adoption 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 12d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When Our PM Started Writing Code: What Merge Rate Taught Us About AI Adoption — Tammuz Dubnov
This skill grounds every response in outline.md, transcript.md, and quote.md from Tammuz Dubnov's talk.
Grounding Workflow
- Check
quote.mdfor a strong pre-extracted quote on the topic. - Read
outline.mdto find the relevant section, framework, or glossary entry. - Read the matching range of
transcript.md. - Verify every quoted phrase appears verbatim in
transcript.mdbefore using quotation marks. - If the located material only partially answers the user, say what the talk covers and what it does not cover.
Key Concepts from the Talk
Use these as anchors when routing user questions to the right section of the transcript:
- Merge Rate — The primary metric Tammuz proposes for measuring AI adoption; tracks how frequently contributors (including non-engineers) successfully merge code.
- Non-technical contributors shipping PRs — The talk's central case study: a PM writing and merging code with AI assistance.
- Harness Engineering — The practice of building internal tooling and scaffolding that allows non-engineers to contribute safely.
- Zero-dev-touch Rate — A metric tracking how often AI-generated contributions require no developer intervention before merging.
- Calamarous Coding — A term from the talk; locate its definition and context in
transcript.mdbefore explaining it. - PM-to-engineer authority collapse — The talk's framing of how AI shifts decision-making and contribution authority across traditional role boundaries.
- AI adoption ROI — Tammuz's argument for how merge rate and related metrics translate AI tooling investment into measurable organisational output.
- AI-native org design — The broader organisational framework the talk proposes, built around these metrics and practices.
Response Formats by Request Type
Factual question about the talk
Tammuz frames the core metric as merge rate: "[safe excerpts from transcript.md]". In the talk, this matters because [one-sentence explanation grounded in the same section].
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 65 lines · 139 tokens per session scan A ef568a317cb5
talk-dubnov-merge-rate-ai-adoption is a skill published in the GitHub repository jscraik/Agent-Skills (8 stars, last pushed 11d ago), licensed Apache-2.0. It adds 139 tokens to every session and 997 once invoked, about $0.0007 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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