rem-page-skill

rem-page-skill is a skill for Claude Code, Codex from openonion/connectonion. It costs 24 tokens per session (1,148 once invoked), scanned A, original, Apache-2.0.

A template and evidence guide for documenting an installed skill. It records what the skill does, when to use it, how reliable it is, and how to start.

In plain words
What is it for?
Use it to create or maintain a skill page with current status, example results, inputs, outputs, performance, limitations, and source links.
Why use it?
It separates advertised capability from observed results, so readers can tell whether a skill has actually been tested. It also keeps limitations and usage history visible.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create or maintain a skill page with current status, example results, inputs, outputs, performance, limitations, and source links.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/openonion/connectonion/rem-page-skill
About the project

ConnectOnion is an open-source, template-first toolkit for building, debugging, deploying, and operating AI agents. Developers use its command-line tools and Python runtime to create agents, add tools, connect services, deploy them, and make them callable by other agents, while the catalogue entries are related agents, skills, and instructions.

openonion/connectonion · 1,497 stars · on GitHub · docs.connectonion.com

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 openonion/connectonion --skill rem-page-skill
Clone the repo
git clone --depth 1 https://github.com/openonion/connectonion

Made for: Claude Code, Codex.

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 rem-page-skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/openonion/connectonion/rem-page-skill/github.svg)](https://agentmods.dev/skills/openonion/connectonion/rem-page-skill)
Your own site
<a href="https://agentmods.dev/skills/openonion/connectonion/rem-page-skill"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/rem-page-skill/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 rem-page-skill

Your own site · 80×15
<a href="https://agentmods.dev/skills/openonion/connectonion/rem-page-skill"><img src="https://agentmods.dev/badge/skills/openonion/connectonion/rem-page-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,148 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 $0.00024 $0.01148
Opus 5.5 $0.00010 $0.00459
Sonnet 5.5 $0.00005 $0.00230
Haiku 4.5 $0.00002 $0.00115

Measured 6d ago against content hash dbfaa06f81e4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

rem-page-skill 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 6d 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.

connectonion/useful_skills/rem-page-skill/SKILL.md · 129 lines

How it starts

The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.

An installed skill's page

Help a reader choose and start a task. Keep the lead short; link, don't paste, the source. State missing execution evidence once in Current status. Keep # title exactly the mapped invocation name, without a descriptive suffix; co rem investigate uses it to find the skill's runs.

Keep the core headings What it does, Insight, When to use, Current status, How to use, Inputs and outputs, Usage history, Limitations, Uncertainties, Source and Sources. Keep other headings only when evidence fills them; omit an optional section whose whole body would be Unknown. Keep the order below:

# <Name>

## What it does

## Insight

## When to use

## Current status

## Example result

## How to use

## Inputs and outputs

## Usage history

## Performance

## Limitations

## Maintenance

## Related projects

## Open threads

## Uncertainties

## Source

## Sources

The short opening

  • Insight: 1–3 cited findings changing task choice, setup or verification. Compare instructions with recorded outputs; name the mismatch, capability or failure condition, consequence and next check. Prefer a specific trap or conflicting rule over listing prerequisites. Put the execution-evidence caveat in Current status, not in each Insight. Counts do not prove success.
  • What it does: one plain sentence: the task and intended outcome. Say the skill aims to produce checked work until an openable artifact proves it did.
  • When to use: a concrete suitable task and an important unsuitable case, when known. Never present invented examples as observed successes.
  • Current status: latest observed run date, version/model, whether the task was completed, actual output, quality assessment, known blockers; a few lines linking the detailed run. With no reviewed run evidence: Unknown — not verified (not "never ran").
  • Example result: a retained, openable artifact or excerpt with its run reference. If only a run report survives, label its output as reported; leave uninspected artifact details Unknown. Label partial results.
  • How to use: shortest verified invocation, prerequisites, working directory, ideally at most three steps. Never expose credentials.
  • Inputs and outputs: required inputs, expected outputs and their locations.

Run evidence and statistics

Usage history: observed runs, recent first: date, task, run ID/link, skill version or source hash, model/configuration, completion, output/artifact, quality assessment, elapsed time, failure reason. Missing fields stay Unknown. Exit status, task completion and quality are three separate things: a clean exit without the artifact is not completion; an unreviewed output is not good. Link older records when numerous.

Performance summarizes observed records, never advertising:

  • Date range, sources, coverage gaps, number of distinct runs (deduplicated by run identity). No records means unknown, not 0% or zero runs.
  • Completed, partial, failed, cancelled, unknown counts. A completion rate states numerator, denominator and exclusions; never hide failures or compare a truncated retry with a completed task.
  • Quality separately, with a named rubric or review basis and number reviewed. No invented numeric score; unreviewed outputs do not pass.
  • Elapsed time and input/output tokens with units and per-metric sample sizes; per-run totals apart from per-call usage. Never infer tokens/second from task duration; use generation timing. Never invent cost or token telemetry.
  • Separate versions, models/context and task types/difficulty; label cache conditions. Never pool unlike runs. Small samples are not guarantees.

Limits and maintenance

  • Limitations: scope, dependencies, known failure patterns and workarounds, each evidenced.
  • Maintenance: verified maintainer/contact, recent dated changes and whether they were validated. Earlier successes do not verify a newer version.
  • Related projects: observed associations and valid links only.
  • Notebook links use notebook paths. Do not copy source-relative ../name/SKILL.md links into the catalog; use a verified catalog link or the plain skill name.
  • Open threads: concrete next actions, owners/dates when known.
  • Uncertainties: missing logs, unread sources, stale results, unresolved identity/version attribution. Do not manufacture a complete-looking dashboard.
  • Source: the map owns it: the source file (linked, never pasted), discovery location, allowed tools and every installed copy, identical or differing by content hash. Leave it as the map wrote it.
  • Sources: numbered references with source id, date, confidence; every investigated claim cited. Mark inference.

Read the full file on GitHub · 129 lines

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. 6d ago Changed · -2 lines dbfaa06f81e4
  2. 13d ago Changed · -1 lines 21f0eef882c5
  3. 14d ago Changed · +6 lines 05e56af08bd1
  4. 16d ago Changed · +3 lines 13bfa6f0c14d
  5. 17d ago First seen · 123 lines · 24 tokens per session scan A d9e46a996aa5

Subscribe to this mod's changes

rem-page-skill is a skill published in the GitHub repository openonion/connectonion (1,497 stars, last pushed today), licensed Apache-2.0. It adds 24 tokens to every session and 1,148 once invoked, about $0.0001 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 2026-09-30.

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