re0-import

re0-import is a skill for Claude Code, Codex from LilMGenius/paperthin. It costs 84 tokens per session (1,694 once invoked), scanned A, original, MIT.

A user-invoked workflow for placing supplied material—such as notes, links, data dumps, or corrections—into the appropriate document of an existing knowledge base. It follows that knowledge base's own routing and writing rules.

In plain words
What is it for?
Use it to import handovers into an existing knowledge base, preview the proposed placement, edit the owning documents, and report unplaced claims.
Why use it?
It prevents new information from being filed in a random neighbouring document or being assigned to an unsupported category. Material that cannot be placed confidently is reported instead of guessed.

Skill for Claude CodeCodex

Written for Claude Code and Codex: disable-model-invocation in frontmatter, but also agents/openai.yaml present.

Part of the paperthin plugin — 45 skills shipped together

Good fit Use it to import handovers into an existing knowledge base, preview the proposed placement, edit the owning documents, and report unplaced claims.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lilmgenius/paperthin/re0-import
About the project

Paperthin is a collection of low-level design patterns that coding agents can apply as repeatable checks and actions during software work. It is for people using agents such as Claude Code, Codex, Cursor, or other supported tools, and its catalogue entries package the patterns as skills, an instruction, and a plugin.

LilMGenius/paperthin · 1,401 stars · on GitHub · paperthin.ai

Install

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.

Any agent
npx skills add LilMGenius/paperthin --skill re0-import
Clone the repo
git clone --depth 1 https://github.com/LilMGenius/paperthin

Made for: Claude Code, Codex.

Or install paperthin, the plugin that ships this one along with the rest of its 45 skills.

Wrote 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.

agentmods badge for re0-import

README.md
[![agentmods](https://agentmods.dev/badge/skills/lilmgenius/paperthin/re0-import/github.svg)](https://agentmods.dev/skills/lilmgenius/paperthin/re0-import)
Your own site
<a href="https://agentmods.dev/skills/lilmgenius/paperthin/re0-import"><img src="https://agentmods.dev/badge/skills/lilmgenius/paperthin/re0-import/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.

agentmods 80×15 button for re0-import

Your own site · 80×15
<a href="https://agentmods.dev/skills/lilmgenius/paperthin/re0-import"><img src="https://agentmods.dev/badge/skills/lilmgenius/paperthin/re0-import.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,694 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00084 $0.01694
Opus 5.5 $0.00034 $0.00678
Sonnet 5.5 $0.00017 $0.00339
Haiku 4.5 $0.00008 $0.00169

Measured today against content hash 197a3246a1a8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

re0-import 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 today.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/guard.mjs, scripts/guard.test.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/breadth/re0-import/SKILL.md · 59 lines

How it starts

The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Land handed-over material in the document of an existing knowledge base that owns it, by rules read from that base.

Goal

Each claim in the material lands at the tier of an existing base that owns its subject, by an edit to that owning document rather than a new neighbour beside it. Placement follows rules the run read from the target at run time. What cannot be placed with confidence is reported, never guessed: the skill neither researches the subject nor invents a tier.

The human names this skill with the material (any shape) and the target root, and may ask for a dry run. The run returns the claim list, edits inside the target root in each destination's grammar (none on a dry run), and the result block.

The skill is user-invoked because landing material in a base is a deliberate human hand-off. A model with this skill in reach would start filing every passing finding into whatever base is open, writing into documents nobody asked it to touch.

Workflow

Phases 1 to 4 run in order. No destination is named before phase 1 completes, and no byte is written before phase 4; a dry run (phase 5) runs phases 1 to 3 and skips phase 4.

  1. Read the map. Find the target's routing documents: the agent-facing map at its root and the map at each tier. Extract each tier's definition with the test that separates it from the others, the ownership rows saying which document owns which subject, and any stated anti-pattern. This is the candidate tier list; a run that skips it is guessing at a structure it never read.

  2. Classify. Give the material one of four shapes:

    • new information: a subject the target does not carry yet;
    • a correction: it contradicts the headline of a live line whose evidence still stands;
    • a bare reference: a link or citation with no framing;
    • a raw dump: several separable claims in one body.

    Split a dump into claims before scoring any destination and route each independently; a claim still carrying two grammars after the split is two claims. Emit the claim list. Material that is one whole item (a skill, module, script or doc section) rather than a set of claims is not a fifth shape: when its fit in the base is in question, call the Skill tool with "scopechk" before placing it, and place it whole only where that verdict allows.

  3. Place. For each claim, name the owning document and quote the phase 1 rule that puts it there; a placement with no quoted rule is not a placement. Apply the confidence floor and write nothing below it. Create a new file only when the run can state why no existing tier holds the claim, and emit that reason. When a claim's subject already lives in two or more documents of the base, report the scatter and call the Skill tool with "ssotize"; do not add a third copy. When the human wants a claim checked before it lands, call the Skill tool with "factchk"; this skill itself never researches.

  4. Write. Before the first byte changes, run node scripts/guard.mjs <target-root> <files-to-edit...> [--create <new-file>]... --json (exit 3 when a path is refused): it returns each file's SHA-256 and the refused paths with reasons. Edit each document in the grammar it already uses, per the grammars reference, then emit the result block: Touched (file and pre-edit SHA-256), Placed (claim, destination, quoted rule), Unplaced (claim, rejected candidates, reason each failed), Created (new file, reason no tier holds it). All four fields are written every run, empty ones included.

  5. Dry run. When the human asks for one, emit the claim list and the result block and edit nothing.

  6. Stop. The diff and the result block go to the human, who decides whether the change is committed. Any claim under the confidence floor stays unplaced for the human to route by hand. When the owning document has drifted beyond the one claim being landed, call the Skill tool with "re0" after the import, as a separate pass.

Read the full file on GitHub · 59 lines

Files

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.

Changes

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.

  1. today First seen · 59 lines · 84 tokens per session scan A 197a3246a1a8

Subscribe to this mod's changes

re0-import is a skill published in the GitHub repository LilMGenius/paperthin (1,401 stars, last pushed yesterday), licensed MIT. It adds 84 tokens to every session and 1,694 once invoked, about $0.0003 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 2026-11-09.

Related

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