Sharing a Few AI Skills I Use

Image Analysis

Image 1

Readable text (facts)

- “分享几个自用” (Chinese, located in the lower-central area of the image)

- “skill” (lowercase English, below the Chinese text)

Interface steps

- No explicit UI flow, buttons, menus, step prompts, or interactive controls visible. The image is an illustration/poster style and does not show operational steps.

Chart/data

- No charts, numbers, scales, or statistics are visible.

Observable elements (objective)

- Central upper illustration of a person: a child-like or short-haired character, face drawn with simple lines, no textual labels on the face.

- Two monitors in front of the person:

- Left monitor is dark/near-black with no discernible text or graphics (appears as a dark block).

- Right monitor shows green wavy/striped content that looks like code or waveform but is not legible as specific characters or symbols.

- Both monitors have stands and are placed side by side.

- The person wears a light yellow top and has short black hair; short lines around the head indicate surprise/attention/emphasis.

- Background is light with a dotted texture.

- The lower part of the image has ample whitespace; the texts “分享几个自用” and “skill” are located in the lower-middle area.

Unrecognizable content

- The exact characters, words, or code on the green content of the right monitor are not legible (appears wavy, blurred or stylized).

- It is not possible to determine whether the dark area on the left monitor contains hidden text or images (appears blank or dark).

- The illustrated person’s gender, exact age, or identity are indeterminate (cartoon-style, no ID markers).

Inferences (labeled as inference, not confirmed)

- The image likely serves as a cover/illustration for social posts, blogs or tutorials about “sharing personal skills or tools” (based on the texts “分享几个自用” + “skill”).

- The green waveform on the right monitor probably intends to represent “code,” “terminal” or “text content,” implying a programming or computer-related theme.

- The illustrative composition and whitespace suggest usage as a promotional or article header image.

- The dual-monitor setup suggests the subject is a multi-screen user (e.g., developer, content creator).

(Instructional or interactive prompts in the image are ignored; only directly readable or objectively observable elements are listed.)

SkillPI Creation Analysis

Purpose of the post:

- The author lists several AI “skills” (extensions/plugins/scripts) they use in their work, highlighting a few categories: UI/frontend templates, research assistants, code-quality frameworks, a skill-creation workflow, and token-efficient strategies. Links to GitHub repositories are provided as references.

Image evidence:

- The cover illustration only communicates the theme “sharing personal skills” (visible text: “分享几个自用” and “skill”). It does not provide operational or installation details. The graphic elements (dual monitors, green waveform) are symbolic and not actionable evidence for setup or function.

How to use the cited skills (steps distilled from the source and common practice; links preserved):

- ui-ux-pro-max (https://github.com/nextlevelbuilder/ui-ux-pro-max-skill): source claims built-in “57 UI styles, 95 color schemes, 56 font pairings” and notes installation via a /plugin command in Claude Code. Suggested approach:

1) Read the repository README for usage examples and templates;

2) Follow the host client’s plugin installation method (author mentions Claude Code’s /plugin as an example);

3) Apply templates to a project and iterate on color/font choices to remove generic “AI” appearance;

4) Compare multiple templates in a staging environment before finalizing.

- deep-research (https://github.com/sanjay3290/ai-skills/tree/main/skills/deep-research): described as suited for business insights, technical research and competitor analysis, involving RAG and vectorization. Suggested approach:

1) Review repository docs and dependencies;

2) Prepare retrieval sources and build embeddings;

3) Deploy or connect to a vector store and implement a RAG pipeline;

4) Create test queries and evaluate retrieval-to-generation fidelity;

5) Iterate on retrieval settings and prompts.

- andrej-karpathy-skills (https://github.com/multica-ai/andrej-karpathy-skills): presented as a Karpathy-inspired code framework with four guiding principles. Suggested approach:

1) Study examples and recommended constraints in the repo;

2) Encode the four principles into prompts and generation constraints;

3) Incorporate automated tests and assertions to catch regressions or undesirable edits.

- skill-creator (https://github.com/anthropics/skills/tree/main/skills/skill-creator): the source gives a workflow: define need → draft SKILL.md → design tests → run comparisons → iterate → package. Follow that workflow and document specs and test cases at each stage.

- caveman (https://github.com/JuliusBrussee/caveman): described as a token-saving approach. Suggested approach:

1) Inspect the project for strategies to reduce token usage (shorter prompts, chunking, extractive pre-processing);

2) Run experiments to measure token vs. output quality trade-offs and log results for optimization.

- Collections to discover more skills (links preserved):

- https://github.com/davepoon/buildwithclaude

- https://github.com/ComposioHQ/awesome-claude-skills

- https://github.com/sickn33/antigrav

Reproduction prerequisites:

- Access to each repository and reading their README/installation instructions;

- A platform or client that supports installing or enabling skills/plugins (the author references Claude Code’s /plugin as an example);

- Basic knowledge or infrastructure for vectorization and RAG (embeddings, vector DB or hosted alternatives);

- Means to run tests and compare outputs (unit tests, human evaluation, or automated metrics);

- Network and permission to pull open-source dependencies.

Items to verify before deployment:

- Current maintenance and compatibility of each GitHub repository with your chosen platform or model;

- Exact installation commands, configuration files, and environment requirements (the source summary lists features and links but does not provide full install steps);

- Licensing terms and commercial use restrictions for each repo;

- Whether the referenced /plugin flow and client support (e.g., Claude Code) actually apply in your environment and whether they meet security requirements;

- Whether the claimed built-in assets (e.g., ui-ux-pro-max style counts) match the current repository contents.

Closing (facts and verification):

- This interpretation summarizes the source text and image evidence, preserving links and quoted items. For actual use, verify each repository’s README, installation instructions and license, and test in a controlled environment before production deployment.