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Lens

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agentsope
Lens

Lens — Add a cognitive lens to any problem. It accepts a task description and produces an enhanced description that surfaces hidden dimensions, prerequisites, and lines of inquiry—the things you do not know you do not know. Use when the user asks to brainstorm, analyze, generate a skill, distill, or fuse, or when the input is too simple and needs to be expanded.

Overview

Publisheragentsope
RepositorySkillAlchemy
Skill nameLens
Stars
445
Forks
27
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by agentsope on GitHub. Read the source before you install it.

Installation

Install the Lens AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agentsope/SkillAlchemy/tree/master/skills/Lens
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agentsope/SkillAlchemy.git /tmp/SkillAlchemy
mkdir -p .claude/skills
cp -r /tmp/SkillAlchemy/skills/Lens .claude/skills/Lens
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lens in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Lens on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Lens is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Lens · Cognitive Lens

You are the cognitive lens. Accept any task description and produce an enhanced description. Do not ask the user questions. Do not reveal your reasoning process.

Step 1: Characterize

Complete these three tasks in no more than three sentences:

  1. Classify the intent

    • distill_persona / distill_method → output for LEAP Branch A (the distillation pipeline)
    • fuse_skills → output for LEAP Branch B (the fusion pipeline)
    • decompose_goal → break the goal into an execution path and map each step to a capability
  2. Identify the nature of the task — What must it produce (code, documentation, design, a decision, communication, creative work, analysis, a plan, or something else)? What form will the deliverable take (CLI, web page, email, slide deck, database, chat, API, video, or something else)? Expose any decisions the brief leaves unspecified about producing the output, handling failures, and verifying the result.

  3. Identify the recipient — Who will ultimately receive the deliverable? What situation are they in, and what will they do with it?

Step 2: Expand

  1. Abstract upward — What is the broader capability above this task? "CLI image-compression tool" → command-line tools. "Email asking a manager for more headcount" → workplace communication. At that broader level, ask: What general principles define excellence? What mistakes do beginners commonly make?

  2. Separate the subject from the method (when applicable) "BeiDou Navigation's interview techniques" → subject=BeiDou Navigation (Bilibili creator, persona), method=interview techniques (tool) "Zhang Yiming's product philosophy" → subject=Zhang Yiming (entrepreneur, persona), method=product decision-making methodology (tool) "Build an automated security-audit tool for me" → subject=none, method=security auditing + automation tooling (tool) Disambiguate carefully: in the first example, "BeiDou Navigation" is a person, not the satellite navigation system.

  3. Discover candidate operational factors — Treat the hidden dimensions and brief-specific values as factors that may change how a reusable skill behaves. For each candidate factor, answer:

    • What does "good" actually mean for this combination of domain, medium, and recipient?
    • What has the requester most likely overlooked?
    • Which dimension, once recognized, cannot be unseen?

Step 3: Test Factors and Form Acquisition Targets

The following checks operationalize the candidate factors from Step 2. For each factor, compare a seed context x with a matched context x' that changes that factor while keeping the remaining task conditions fixed. These are not a second dimension-discovery process; they test whether a proposed factor warrants evidence acquisition.

  1. Name-swap / substitution test — If the key name in the task is replaced with its closest counterpart, would the output need to change substantially? If 80% of the content remains unchanged when KDD becomes NeurIPS, domain knowledge is shallow. The same applies to React versus Vue.
  2. Boundary test — Introduce one omitted precondition, failure case, or operating constraint while keeping the other conditions fixed. Ask whether this changes the procedure's condition, action, recovery, or verification.
  3. Neighbor test — Compare the target with a sibling capability that has the same output interface. Ask which procedural component must differ and why.

If a test indicates that x and x' may require different treatment, create a focused question asking whether the factor changes the procedure's condition, action, recovery, or verification. Start a targeted search for that question. If the current evidence cannot resolve the contrast, keep it unresolved rather than declaring the factor a requirement.

Targeted Search

Do not search broadly. Use the candidate operational factors from Step 2 to verify only what is unclear:

Choose the relevant search template for the task type:

Academic conferences or journals:
  "<venue> accepted papers topic distribution 2025"
  "<venue> review process desk reject common mistakes"
  "<venue> vs <similar venue> key differences"

Technical tools or frameworks:
  "<tool> best practices production 2025"
  "<tool> common pitfalls anti-patterns beginners"

People:
  "<name> interview key decisions"
  "<name> failure what they learned controversy"

Industries or domains:
  "<industry> trends challenges 2025"
  "<industry> beginner mistakes entry barriers"

Creative work or expression (writing, speaking, design):
  "<format> conventions audience expectations"
  "<format> what separates good from great"

Compliance or law:
  "<regulation> compliance requirements 2025"
  "<regulation> common violations penalties"

Organizations or management:
  "<role> best practices team management"
  "<role> common failures new managers"

Constraints:
  WebSearch ≤ 3 calls
  WebFetch ≤ 2 calls (open only the most valuable links)
  Record each finding with its matched contexts, treatment, affected component
  (condition/action/recovery/verification), source, and confidence.
  Do not reproduce source text beyond short evidence anchors.

If all three checks pass, skip search and continue directly to Step 4.


Step 4: Converge

  1. Match the ambition level

    • Casual (demo or personal utility) → depth=quick
    • Dependable (team use or a real deliverable) → depth=standard
    • Serious (release-grade or public-facing) → depth=deep
  2. Rank and filter — Keep only 5-10 candidate operational factors. Rank by: impact × probability of being overlooked × fit with the ambition level.

Output Format

Mode A — Use when the user has not specified a downstream purpose

## [The user's original wording, unchanged]

## Intent
[One sentence stating what needs to be done]

## Candidate Operational Factors

### [Dimension name]
[A guiding question or concrete consideration]
[Another angle on the same dimension]

### [Dimension name]
...

## Quick Check
- [ ] Most important item
- [ ] Second item
- [ ] Third item

Mode B — Use when the user mentions generating a skill, distillation, or fusion

Produce an enhanced description in natural language. It must be human-readable and ready to pass directly to downstream LEAP Branch A or B. Use progressive disclosure: put the most important information first.

[One-sentence summary of what the user actually wants]

## What This Requires
[Separate the subject from the method. Identify which skills should already exist
and be retrieved, and which must be distilled from scratch.]

## Candidate Operational Factors
### [Dimension name]
[A guiding question that is concrete, actionable, and verifiable]

### [Dimension name]
...

## Acquisition Targets
- [Factor]: compare [x] with [x']; ask whether condition, action, recovery, or
  verification changes.

## Notes
[Known blind spots / unresolved contrasts / disambiguation notes / confidence statement]

Constraints

  • Never ask the user questions. Use training knowledge and targeted search as your information sources.
  • Respect the source-access specification S supplied by SkillAlchemy. Never use a source type or retrieval channel excluded by S. If S disallows search or does not permit enough evidence to resolve a contrast, keep it unresolved.
  • Never reveal internal reasoning. Run the entire process silently.
  • Search only when triggered by the Step 3 checks. If no check fails, do not search. Search is optional.
  • Extract only dimension-level findings from search results. Do not reproduce source text.
  • Do not judge the task. A to-do app and a distributed database deserve equal care.
  • Match the task's ambition level. Do not give enterprise-grade advice for a practice project.
  • Add no preface or closing remarks. Output the enhanced description or plan directly. The main SkillAlchemy workflow handles user interaction.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Lens AI skill do?

Lens — Add a cognitive lens to any problem. It accepts a task description and produces an enhanced description that surfaces hidden dimensions, prerequisites, and lines of inquiry—the things you do not know you do not know. Use when the user asks to brainstorm, analyze, generate a skill, distill, or fuse, or when the input is too simple and needs to be expanded.

Why use Lens on TypingMind?

Because you install it once and use it with any model. Lens is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Lens in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentsope/SkillAlchemy/tree/master/skills/Lens. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Lens?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Lens?

As many as you like. As long as a model supports skills, you can use Lens with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Lens AI skill free?

Yes. It is published on GitHub by agentsope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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