Scientific Agent Skills

Image Analysis

Image 1

Readable text (verbatim, top-to-bottom, left-to-right as discernible):

- Browser/window top (partially readable): "scientific-agent-skills" (visible in the address bar or tab area)

- Large central text (orange brush background, three lines):

- "科研Agent 技能库"

- "135个科研技能"

- "135个科研技能"

- Lower-left/background (blurred, partial file/code list fragments):

- Discernible fragment: "__init__" (possibly part of __init__.py, suffix not fully confirmed)

- Other text lines are blurred and not fully readable

Unreadable or uncertain text (explicitly labeled):

- The rest of the browser address bar, toolbar icons, and labels (e.g., possible URL, bookmarks, extensions) cannot be fully read; only the fragment "scientific-agent-skills" is visible.

- The code/file list beneath the orange overlay is mostly blurred; specific filenames and code lines cannot be read verbatim.

- Possible window controls or tab names in the top-right/left are blurred and unreadable.

UI/interaction cues visible in the image:

- The image appears to be a desktop app or browser screenshot (there is a window border, address/tab area, and content area).

- The page main area is covered by a large banner showing a title and a count/quantity.

- The background resembles a code editor or file browser (multiple lines of small text, file-list style with line separators), but no explicit interaction steps (clicks, menus) are shown.

Chart/data:

- No standard charts (lines, bars, etc.) or numeric tables are visible; only large text stating "135个科研技能" is present.

Visual features (layout, color):

- Overall color: white background with a large orange brush-style banner.

- Typography: large bold Chinese/English mixed title ("Agent" in English) with black text and white outline/shadow, placed on the orange brush.

- The orange banner appears in three segments: top with title ("科研Agent 技能库"), middle and bottom each showing "135个科研技能".

- Background is a light-colored editor/web page view with many small text lines or filenames, blurred by the overlay.

- Window border and system UI are visible around the image but details are blurred.

Facts vs. inferences (clearly separated):

- Facts (directly readable from the image):

- Center text reads: "科研Agent 技能库" (mixed Chinese and English).

- The image clearly shows two occurrences of the text "135个科研技能".

- The top shows the fragment "scientific-agent-skills" (likely an address/tab or project name fragment).

- Background looks like a code/file view, though most text is obscured.

- Visual layout includes a large orange brush banner covering the center.

- Inferences (based on the image but not determinable solely from it):

- The image may be a project or repository page named "scientific-agent-skills" (inference, since only a fragment is visible).

- Background filenames may include Python files (e.g., __init__.py) or README, but full names and suffixes cannot be confirmed.

- The image may be used as a promotional cover showing the quantity "135个科研技能", but its exact purpose cannot be confirmed from the image alone.

Other notes:

- Instructional text visible in the image has been omitted per requirements.

- The image does not provide a full clickable or copyable link; incomplete sources are labeled as unreadable/incomplete.

SkillPI Creation Analysis

Purpose and value: The project described aims to convert common research tasks (data retrieval, experimental analysis, modeling, manuscript drafting, clinical-research steps) into reusable Agent Skills so LLM-driven tools (the summary mentions Codex, Claude Code, Cursor) can call them per scenario. This reduces time spent reading docs and integrating tools, and makes workflows auditable and extensible. Original source URL: https://www.douyin.com/video/7639901177580113187.

Key image evidence:

- The screenshot prominently displays “科研Agent 技能库” and two occurrences of “135个科研技能”, with the fragment “scientific-agent-skills” visible in the page header and a code/file view in the background. These elements support the claim of an organized skill collection of a specific size (135), but the actual repository or page should be checked to confirm.

Practical steps to use or verify the Skills (actions you can take based on the provided description):

1) Obtain the project source: locate and clone/download the full project or repository (the image shows only the fragment “scientific-agent-skills”; you must find the complete URL or repository to proceed).

2) Inspect SKILL.md files: the summary states each Skill includes a SKILL.md and code examples—open those files to learn inputs, outputs, dependencies, and invocation examples.

3) Prepare the runtime environment: install required dependencies per SKILL.md (commonly Python, scientific libraries). Drug-discovery skills may require RDKit, DeepChem, DiffDock, OpenMM; bioinformatics skills may need API access to NCBI, UniProt, ChEMBL, ClinicalTrials.gov.

4) Run example code: execute provided examples and capture logs/output for verification.

5) Integrate with your agent platform: wrap Skills as callable functions/actions for your chosen agent/LLM system (examples mentioned are Codex, Claude Code, Cursor), then test an end-to-end workflow (e.g., query NCBI -> parse results -> use RDKit for molecule filtering -> generate report).

Prerequisites for reproduction:

- Access to the complete project source or release page (the screenshot alone is insufficient);

- Presence of SKILL.md and working code examples inside the project;

- Proper runtime and dependency installation (Python, RDKit/DeepChem/OpenMM where required);

- API access or ability to query external services (NCBI, UniProt, ChEMBL, ClinicalTrials.gov) when a Skill depends on them;

- An agent/LLM platform that can call external functions or services (integration capability with Codex/Claude Code/Cursor or similar).

Items to verify before adoption:

- Confirm the repository indeed contains 135 Skill entries and inspect their directory structure;

- Validate each Skill’s SKILL.md for completeness (inputs, outputs, example usage, expected side effects);

- Check third-party tool compatibility and licensing (RDKit, DeepChem, OpenMM installation and license constraints);

- Verify whether the project includes direct integration examples or adapters for Codex/Claude Code/Cursor (the summary claims these tools can be used, but concrete adapter code should be located and tested);

- Assess data privacy and compliance implications when Skills query clinical or sensitive datasets.

Closing verification note: Use the repository or project release page as the authoritative source to confirm the “scientific-agent-skills” project structure, the claim of 135 skills, and the presence of SKILL.md and runnable examples. The above steps are based on the provided summary and screenshot OCR and are intended for initial validation and implementation planning rather than a claim of completed integration.