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 ronaknnathani/relay --skill build-write-like-megit clone --depth 1 https://github.com/ronaknnathani/relayWrote 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/ronaknnathani/relay/build-write-like-me)<a href="https://agentmods.dev/skills/ronaknnathani/relay/build-write-like-me"><img src="https://agentmods.dev/badge/skills/ronaknnathani/relay/build-write-like-me/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/ronaknnathani/relay/build-write-like-me"><img src="https://agentmods.dev/badge/skills/ronaknnathani/relay/build-write-like-me.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 | $0.00226 | $0.03242 |
| Opus 5 | $0.00113 | $0.01621 |
| Sonnet 5 | $0.00045 | $0.00648 |
| Haiku 4.5 | $0.00023 | $0.00324 |
Grade A, and why
build-write-like-me 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write-Like-Me Builder
This skill builds a personalized writing-voice skill for one person. The output
is a SKILL.md that any agent can load to draft or edit prose in that person's
voice. You produce it by gathering the person's real writing from sources they
provide, analyzing it (both statistically and by reading it), and synthesizing a
profile grounded in real examples.
The core principle: a voice profile is only as good as the real writing behind it. Don't invent traits. Every claim in the generated skill should trace to something the person actually wrote. Quote them verbatim wherever you can.
The workflow
- Collect sources from the invoker (interactive prompt).
- Set up the workspace (an output skill dir with a
source/subdir for raw data). - Gather raw data into
source/: {{subagent:large_context}} for delegated source workers where available, and use bundled scripts. - Analyze the data: run stylometrics, filter out content the person didn't actually write, and extract verbatim voice samples per register.
- Synthesize the personalized
SKILL.mdvoice profile. - Self-consistency check and present.
Do these in order. Steps 3 and 4 are where delegated workers do the heavy lifting so the raw data never floods your context.
Step 1: Collect sources
Ask the invoker which sources they want to include using whatever user-question mechanism is available (multi-select if supported; otherwise ask a concise numbered question). Don't assume anything about their environment or what's installed — different users have different MCP servers and skills. Offer:
- GitHub — PR descriptions and review/issue comments they authored. Need their GitHub login and the org(s)/repo(s) to search. Usually the richest source of working voice.
- Google Docs — design docs, proposals, strategy docs, feedback. They provide doc links/IDs or a folder.
- Local filesystem — a blog repo, a docs directory, exported notes, an
.mbox, a Slack export, anything on disk. They provide the path(s). Always offer this — it is the most portable source and sidesteps every tooling gap. - Session transcripts — their own typed messages to coding agents. Great for the casual/chat register. Yes/no.
- Slack — yes/no.
- Email — yes/no.
What ships with it
6 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.
- 10d ago First seen · 289 lines · 226 tokens per session scan A 6d12854e6931
build-write-like-me is a skill published in the GitHub repository ronaknnathani/relay (3 stars, last pushed 7d ago), licensed MIT. It adds 226 tokens to every session and 3,242 once invoked, about $0.0011 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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