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.
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 openonion/connectonion --skill rem-page-skillgit clone --depth 1 https://github.com/openonion/connectonionWrote 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/openonion/connectonion/rem-page-skill)<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.
<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>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 | $0.00024 | $0.01148 |
| Opus 5.5 | $0.00010 | $0.00459 |
| Sonnet 5.5 | $0.00005 | $0.00230 |
| Haiku 4.5 | $0.00002 | $0.00115 |
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.
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 inCurrent 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.mdlinks 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.
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.
- 6d ago Changed · -2 lines dbfaa06f81e4
- 13d ago Changed · -1 lines 21f0eef882c5
- 14d ago Changed · +6 lines 05e56af08bd1
- 16d ago Changed · +3 lines 13bfa6f0c14d
- 17d ago First seen · 123 lines · 24 tokens per session scan A d9e46a996aa5
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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