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 oomol-lab/skills --skill oo-algo-docsgit clone --depth 1 https://github.com/oomol-lab/skillsWrote 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/oomol-lab/skills/oo-algo-docs)<a href="https://agentmods.dev/skills/oomol-lab/skills/oo-algo-docs"><img src="https://agentmods.dev/badge/skills/oomol-lab/skills/oo-algo-docs/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/oomol-lab/skills/oo-algo-docs"><img src="https://agentmods.dev/badge/skills/oomol-lab/skills/oo-algo-docs.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 | $0.00048 | $0.00940 |
| Opus 5.5 | $0.00019 | $0.00376 |
| Sonnet 5.5 | $0.00010 | $0.00188 |
| Haiku 4.5 | $0.00005 | $0.00094 |
Grade C, and why
oo-algo-docs scanned grade C with 2 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://cli.oomol.com/install.sh | bash # macOS / Linux Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://cli.oomol.com/install.sh | bash # macOS / Linux Copies of this mod
1 near-identical copy found in the catalogue:
- oo-bigmailer — 86% identical, 49 lines differ
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlgoDocs
Operate AlgoDocs through your OOMOL-connected account. This skill calls the algo_docs connector with the oo CLI; OOMOL injects credentials server-side, so you never handle raw tokens.
Running an action
Assume the user has already installed the oo CLI, signed in, and connected AlgoDocs. Do not run oo auth login or open the connection URL proactively — just run the action. Fall back to First-time setup only when a command actually fails with an auth or connection error.
1. Inspect the contract to get the authoritative input/output schema before building a payload:
oo connector schema "algo_docs" --action "<action_name>"
2. Run the action with a JSON payload that matches the input schema:
oo connector run "algo_docs" --action "<action_name>" --data '<json>' --json
--datatakes a JSON object string or@path/to/file.json; omit it to send{}.- The response is
{ "data": ..., "meta": { "executionId": "..." } }; the execution id lives undermeta.executionId.
Each action is listed below with a one-line description; actions that change state carry a [write] or [destructive] tag. Before constructing --data, fetch the action's live schema with oo connector schema to get its authoritative input fields.
Available actions
get_extracted_data_by_document— Get extracted data records for one AlgoDocs document ID.get_me— Get the AlgoDocs account identity for the connected API key.list_extracted_data— List extracted data records for an AlgoDocs extractor, optionally filtered by folder, limit, and upload date.list_extractors— List document data extractors in the connected AlgoDocs account.list_folders— List folders in the connected AlgoDocs file manager.upload_document_from_url— Import a publicly accessible document URL into AlgoDocs for an extractor and folder. [write]
Safety
- Untagged actions are reads (get / list / search) — safe to run directly.
- Actions tagged
[write]change AlgoDocs state — confirm the exact payload and effect with the user before running. - Actions tagged
[destructive]remove or overwrite data — always confirm the target and get explicit approval first.
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 Changed · -1 lines a4fb06ab6d35
- 1mo ago Changed · +1 lines dd9855631be2
- 1mo ago First seen · 84 lines · 48 tokens per session scan C b2926d0bb564
oo-algo-docs is a skill published in the GitHub repository oomol-lab/skills (2 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 940 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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