Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add OC-NeuralSense/reader-first-writing-skills/plugin install reader-first-writingWrote 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/oc-neuralsense/reader-first-writing-skills/adapt-to-reader)<a href="https://agentmods.dev/skills/oc-neuralsense/reader-first-writing-skills/adapt-to-reader"><img src="https://agentmods.dev/badge/skills/oc-neuralsense/reader-first-writing-skills/adapt-to-reader/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/oc-neuralsense/reader-first-writing-skills/adapt-to-reader"><img src="https://agentmods.dev/badge/skills/oc-neuralsense/reader-first-writing-skills/adapt-to-reader.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 | $0.00176 | $0.02059 |
| Opus 5 | $0.00088 | $0.01030 |
| Sonnet 5 | $0.00035 | $0.00412 |
| Haiku 4.5 | $0.00018 | $0.00206 |
Grade A, and why
adapt-to-reader 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
adapt-to-reader
Purpose
Recalibrate an existing document for a new target reader (different expertise, different mastered vocabulary, different assumed ground) while keeping every technical concept exactly what it was. This is a revisional, precision-anchored skill: it changes the pitch (jargon, depth, what is spelled out, which blind spots are closed) but treats the denotation of each technical term as inviolable. It emits the recalibrated version, a change-report, and a precision_report.
When to use
- The audience is changing (expert -> lay, practitioner -> executive, or the reverse) and the jargon/depth must be re-pitched.
- A "simplify but keep the meaning" request where the risk is silent concept drift.
When NOT to use (routing non-triggers)
- Same reader, just clearer or shorter sentences -> revise-prose.
- The structure is the problem -> revise-structure.
- You want two versions judged, not one produced -> compare-versions.
- No settled target reader yet -> frame-the-brief first.
Inputs
draft(required)target_reader_profile(required: the new reader-frame or its core fields)technical_definitions(required: the exact sense of each at-risk term, so a substitution can be checked against an anchor rather than guessed)
Workflow
- Diff the readers. Compare source reader to target: expertise, mastered vocabulary, assumed prior knowledge, standing question.
- Inventory at-risk terms. List every technical term, acronym, and precise qualifier the recalibration might touch; attach each to its definition anchor.
- Recalibrate jargon. For the target's in-group, leave entrenched terms undefined; for outsiders, define on first use in-line, expand abbreviations, or substitute a transparent label: only when the plain word denotes exactly the same concept. When a plain substitution would narrow or widen, keep the precise term and gloss it instead.
- Rescale depth and ground. Add or remove spelled-out inference and given ground to match the target's gap; close the blind spots the new reader has.
- Precision pass. For each at-risk term, confirm the post-version still denotes the anchored concept: no scope, tolerance, or category silently changed.
- Emit the recalibrated version, change-report, and precision_report.
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 · 165 lines · 176 tokens per session scan A 13c39ff76494
adapt-to-reader is a skill published in the GitHub repository OC-NeuralSense/reader-first-writing-skills (1 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 176 tokens to every session and 2,059 once invoked, about $0.0009 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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