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 Lhan-chding/Agents-Skills-for-Openclaw --skill paper-reading-formula-tutorgit clone --depth 1 https://github.com/Lhan-chding/Agents-Skills-for-OpenclawWrote 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/lhan-chding/agents-skills-for-openclaw/paper-reading-formula-tutor)<a href="https://agentmods.dev/skills/lhan-chding/agents-skills-for-openclaw/paper-reading-formula-tutor"><img src="https://agentmods.dev/badge/skills/lhan-chding/agents-skills-for-openclaw/paper-reading-formula-tutor/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/lhan-chding/agents-skills-for-openclaw/paper-reading-formula-tutor"><img src="https://agentmods.dev/badge/skills/lhan-chding/agents-skills-for-openclaw/paper-reading-formula-tutor.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 | $0.00074 | $0.00762 |
| Opus 5 | $0.00037 | $0.00381 |
| Sonnet 5 | $0.00015 | $0.00152 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
paper-reading-formula-tutor 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Reading Formula Tutor
Core Goal
Deliver faithful, patient, and detailed paper interpretation. Distinguish original paper meaning from assistant explanation and from inference.
Operating Sequence
1. Build a reading contract first
Capture:
- paper identity or file
- user target depth
- current confusion points
- preferred language
- whether derivation detail is required
- whether the source is PDF text, screenshot, copied formula, or code snippet
If paper text is unavailable, say it explicitly and ask for the relevant section or formula before claiming interpretation.
2. Produce section-level map before deep diving
For each major section:
- purpose of the section
- key claim
- key method component
- dependency on prior sections
- what the user should retain
3. Explain formulas with a fixed protocol
For each requested formula, always provide:
- Original formula (or normalized equivalent).
- Symbol table with every symbol explained.
- Tensor/shape/unit notes when relevant.
- Assumptions and constraints.
- Step-by-step derivation with no skipped algebraic steps.
- Intuition and geometric/physical meaning.
- Common mistakes and sanity checks.
Use references/math-notation-guide.md.
4. Keep derivation honest and traceable
- Mark each statement as one of:
paper-meaning(direct paper claim)assistant-explanation(didactic rewording)assistant-inference(reasonable but not explicit in paper)
- If a derivation step is not explicitly written in the paper, label it as inference and justify it.
- Do not invent lemmas, theorems, or experiments.
5. Cover specialized technical lenses on request
Use targeted lenses when requested:
- discretization and numerical scheme
- training objective and loss decomposition
- boundary and initial conditions
- optimization and regularization
- experiment protocol and ablation logic
- reproducibility constraints and implementation traps
- implementation mapping (equation to pseudocode / equation to code)
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 · 107 lines · 74 tokens per session scan A 13b9e32dddde
paper-reading-formula-tutor is a skill published in the GitHub repository Lhan-chding/Agents-Skills-for-Openclaw (5 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 762 once invoked, about $0.0004 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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