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 ModelVoyager/ReadingTree --skill reading-treegit clone --depth 1 https://github.com/ModelVoyager/ReadingTreeWrote 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/modelvoyager/readingtree/reading-tree)<a href="https://agentmods.dev/skills/modelvoyager/readingtree/reading-tree"><img src="https://agentmods.dev/badge/skills/modelvoyager/readingtree/reading-tree/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/modelvoyager/readingtree/reading-tree"><img src="https://agentmods.dev/badge/skills/modelvoyager/readingtree/reading-tree.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 | $0.00044 | $0.01884 |
| Opus 5.5 | $0.00018 | $0.00754 |
| Sonnet 5.5 | $0.00009 | $0.00377 |
| Haiku 4.5 | $0.00004 | $0.00188 |
Grade A, and why
reading-tree 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 1mo 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reading Tree
Use this skill when the user wants to transform an article or chapter into a linked reading interface:
- The tree must preserve the source order.
- Clicking a node must jump to and highlight the covered source paragraphs.
- Clicking a source paragraph must jump back to the best matching node.
- The result must stay suitable for full reading, not just skimming.
Outcome
Produce a workspace that can build:
site/data/source.jssite/data/tree-data.js- a static interactive site in
site/
The generated site includes:
- bidirectional node/paragraph linking for reading
- global search where text and outline labels use JavaScript regex, while roles stay plain
ANDas the default search operator, with explicit uppercaseOR- optional field prefixes such as
role:example
Shared tooling lives in scripts/.
For online tools such as ChatGPT or Claude.ai that cannot serve the generated UI directly, provide a .zip archive of the full site/ folder and tell the user to extract it and open site/index.html in a browser. Do not hand off only index.html, because it depends on sibling assets.
The default site template opens locally, but it is not fully self-contained in the strict offline/privacy sense: when online, it requests Google Fonts for typography. If those requests are blocked, the browser falls back to local serif fonts and the UI still works.
The reusable workspace scaffold lives in assets/workspace-template/.
Workflow
- Copy
assets/workspace-template/into a fresh workspace. - Save the source text as
article.txt, preserving the original paragraph boundaries by default. Prefer one source paragraph per line. Only split one original paragraph into multiple lines when it is very long or contains an obvious internal semantic shift. - Inspect
article.txtwith real line numbers from the terminal, for examplenl -ba article.txt. - For long files, inspect windows such as
sed -n '120,180p' article.txt | nl -ba -v120instead of counting lines in model context. - Create
splits.jsonwith semantic paragraph start lines only. Ifarticle.txtalready uses one source paragraph per line, the split list will often start on every non-empty line. - Run
python3 reading-tree/scripts/build.py <workspace> --step sourceand inspectsite/data/source.js. - Create
tree.jsonafter the paragraph split is stable. Every node must declare at least one non-empty broad role viaroles. - Run a structure audit against
references/authoring.md, especiallyBreadth ControlandFinal Structure Check. - Materialize that review as a markdown table before continuing. Use one row per non-leaf sibling group in tree order so grouping choices are explicit. Top-level nodes are mandatory rows.
- Regroup until the structure audit table is clean.
6is a ceiling, not a target for normal-length texts. For substantially longer texts, treat the breadth numbers as defaults rather than hard caps, but justify any exception in the audit table. - Prefer adding intermediate grouping nodes over coarsening paragraph splits when reducing breadth.
- During structural iteration, run
python3 reading-tree/scripts/validate.py <workspace>after each structural edit as cheap feedback. - Run a mandatory final label lint pass against
references/authoring.md, especially theMeaning Fidelity CheckandFinal Label Checksections. Inspect every node label one by one in tree order. - Materialize that review as a markdown table before continuing. Use one row per label, and include both style columns and meaning-fidelity columns from
references/authoring.md, plus the roles columns for every row. Treat the table as the required reasoning scaffold, not as optional presentation. - Rewrite every weak or semantically overstated label before continuing. Update the table until every row passes. Do not continue if even one row still fails.
- After the structure audit table and label audit table are both clean, run
python3 reading-tree/scripts/validate.py <workspace>again as the required final validation pass. - Run
python3 reading-tree/scripts/build.py <workspace> --step all. - To inspect or hand off the result, note that
site/index.htmlcan be opened directly in a browser, orcd site/and serve it withpython3 -m http.server 8000 --bind 127.0.0.1. For online tools such as ChatGPT or Claude.ai that cannot serve the UI themselves, provide a.ziparchive of the fullsite/folder and instruct the user to extract it and opensite/index.html. Do not provide onlyindex.html. Also mention that the default template requests Google Fonts when online; if those requests are blocked, the browser falls back to local serif fonts. - If serving locally, tell the user to open
http://127.0.0.1:8000/in a browser. Only launch a browser from the agent if the user explicitly asks for that.
What ships with it
7 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.
- 1mo ago First seen · 91 lines · 44 tokens per session scan A d858a6a65903
reading-tree is a skill published in the GitHub repository ModelVoyager/ReadingTree (2 stars, last pushed 6mo ago), licensed MIT. It adds 44 tokens to every session and 1,884 once invoked, about $0.0002 per session on Opus 5.5. 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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