Open Science is a local-first, model-agnostic workbench for reproducible scientific research. Scientists use its AI agents, Python and R execution, data connectors, and traceable outputs for tasks such as literature review, analysis, simulation, and visualization across macOS, Windows, and Linux.
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 aipoch/open-science --skill self-awarenessgit clone --depth 1 https://github.com/aipoch/open-scienceWrote 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/aipoch/open-science/self-awareness)<a href="https://agentmods.dev/skills/aipoch/open-science/self-awareness"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/self-awareness.svg" alt="Measured on agentmods" 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 | $0.00069 | $0.02620 |
| Opus 5 | $0.00034 | $0.01310 |
| Sonnet 5 | $0.00014 | $0.00524 |
| Haiku 4.5 | $0.00007 | $0.00262 |
Grade A, and why
self-awareness 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 4d 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-awareness
Use repl_execute for every host.* call. The host object exists only in the persistent
JavaScript control REPL; Python and R data kernels do not receive it.
Inspect available capabilities
const caps = await host.capabilities()
The current project-native result contains 20 known boolean keys:
mcpgates connector calls throughhost.mcp(server, method, args?).computegates thehost.computenamespace.agentsgates thehost.agentsnamespace.skillsgates thehost.skillsnamespace.artifactsgates managed-file discovery throughhost.artifacts(options?)and exact path resolution throughhost.artifactPath(versionId).lineagegates the read-onlyhost.lineagenamespace.framesgates the read-onlyhost.framesnamespace.sessionsgates Main-only, read-only Session diagnostics throughhost.sessions.list(options?)and exact lookup throughhost.sessions.inspect(sessionId)in the current Project.llmgates one-shot, tool-less inference throughhost.llm(request, options?).currentModelgates exact current-model lookup throughhost.currentModel(). It returns the calling Session's exact current model id and fails when the live backend cannot establish one.listModelsgates configured Host LLM model discovery throughhost.listModels(). It returns the frozen, stable-sorted configured model ids for the current Host LLM Provider and framework. It never refreshes over the network or merges ids across Providers.viewImagegates transient image attachment throughhost.viewImage(source, options?). Sources may be an Artifact or Upload Version in the current Project, or a path relative to the current execution workspace. For a generated file, pass the same relative path used to save it.delegate,children,collect,stopChild, andresolveMessageare Main/root-only delegated work operations.sendFrameMessageandmessageReceiptare available to Main/root and Delegate agents when their trusted route is provisioned.submitOutputis available only to an authenticated Delegate Attempt with an admitted output schema.
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.
- 4d ago Changed · +1 lines 68907d4d42c3
- 7d ago First seen · 222 lines · 69 tokens per session scan A ce347c966cef
self-awareness is a skill published in the GitHub repository aipoch/open-science (3,699 stars, last pushed today), licensed Apache-2.0. It adds 69 tokens to every session and 2,620 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
Other skills, from other repositories
conventions
Calkit conventions and foundational context. Load whenever working in a Calkit project—defines calkit.yaml structure, environments, pipeline stages, CLI commands, and version control conventions.
build-paper-pipeline
Build the pipeline stages a manuscript's TODO comments ask for.
create-pipeline
Convert an existing repo with ad hoc scripts into a fully reproducible Calkit pipeline. Use when the user invokes /calkit:create-pipeline or asks to make a project reproducible end-to-end.
add-pipeline-stage
Add a single new stage to an existing Calkit pipeline. Use when the user invokes /calkit:add-pipeline-stage or asks to add a script, notebook, or command to the pipeline.
check-reproducibility
Check whether a project is fully traceable, and fix what isn't, including numbers typed into a manuscript that no pipeline output accounts for. Use when the user invokes /calkit:check-reproducibility, asks whether a project is reproducible, or asks where a number in a paper came from.
check-questions
Review a Calkit project's questions and answers against their evidence. Use when the user invokes /calkit:check-questions, asks whether the project's answers are still true, or after a pipeline run changes results that answers cite.