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 JinPLu/clear-eyed-reading-skills --skill clear-eyed-deep-readinggit clone --depth 1 https://github.com/JinPLu/clear-eyed-reading-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/jinplu/clear-eyed-reading-skills/clear-eyed-deep-reading)<a href="https://agentmods.dev/skills/jinplu/clear-eyed-reading-skills/clear-eyed-deep-reading"><img src="https://agentmods.dev/badge/skills/jinplu/clear-eyed-reading-skills/clear-eyed-deep-reading/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/jinplu/clear-eyed-reading-skills/clear-eyed-deep-reading"><img src="https://agentmods.dev/badge/skills/jinplu/clear-eyed-reading-skills/clear-eyed-deep-reading.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 | $0.00117 | $0.03717 |
| Opus 5 | $0.00059 | $0.01858 |
| Sonnet 5 | $0.00023 | $0.00743 |
| Haiku 4.5 | $0.00012 | $0.00372 |
Grade A, and why
clear-eyed-deep-reading 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- clear-eyed-reading — 86% identical, 24 lines differ
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shared Clear-Eyed Reading Core
Help a reader with no prior exposure understand the work and take back interpretive and evaluative control from the authors' terminology, branding, and narrative. Demystify by completing four judgments: reconstruct the ordinary mechanism; trace where capabilities or knowledge come from; identify the genuine increment over the closest prior work; and explain how the reported result came about, whether the conclusion is warranted, or what lets a system do what it shows. Let this independent reconstruction raise or lower the evaluation. Critique claims and evidence, never the authors.
Four judgments
Keep these four judgments, and do not substitute a summary, a limitation list, or a popularity report for any of them:
- Ordinary mechanism — after names and branding are removed, what enters, what happens, and what leaves.
- Capability sources — which data, prior models, assumptions, rules, labels, human choices, instruments, implementation, post-processing, scale, or evaluation choices supply the observed ability.
- Genuine increment — for every important candidate contribution, what is inherited, what technically changed relative to the closest primary precedent, and what the field would lose without that change.
- Result attribution — what actually carries the result or conclusion, and which rival explanations remain consequential.
Scale verification to risk and depth
Identify the work and the version the conclusion will rest on. Never fill gaps from an abstract, caption, search snippet, or guess; say what cannot be checked.
Scale how far to read and search by two factors: conclusion risk (how much the four judgments would change if this were wrong) and the active depth profile. High-risk conclusions—technical novelty, field significance, that the new method caused the result, reuse readiness, claim stability, or that a weakness is absent—need correspondingly stronger primary evidence. The depth profile sets the search budget; it does not change what the four judgments mean.
What ships with it
1 file 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 · 131 lines · 117 tokens per session scan A 21ec04271b31
clear-eyed-deep-reading is a skill published in the GitHub repository JinPLu/clear-eyed-reading-skills (4 stars, last pushed 26d ago), licensed MIT. It adds 117 tokens to every session and 3,717 once invoked, about $0.0006 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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