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Get Started Free →Lens — 给你的问题加一层认知镜片。输入任意任务描述,输出增强版 description, 发现「你不知道自己不知道」的隐性维度、前置条件和认知路线。 Use when 用户说「帮我想想」「分析一下」「生成 skill」「蒸馏」「融合」 或输入看起来太简单需要展开。
.claude/skills/agentsope-lens/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 86% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 90% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 126% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 29% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 56% | 0% |
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.
Complete these three tasks in no more than three sentences:
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 capabilitydesign, 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.
situation are they in, and what will they do with it?
"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?
"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.
brief-specific values as factors that may change how a reusable skill behaves. For each candidate factor, answer:
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.
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.
constraint while keeping the other conditions fixed. Ask whether this changes the procedure's condition, action, recovery, or verification.
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.
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.
depth=quickdepth=standarddepth=deepRank by: impact × probability of being overlooked × fit with the ambition level.
## [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 itemProduce 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]information sources.
S supplied by SkillAlchemy. Never use asource type or retrieval channel excluded by S. If S disallows search or does not permit enough evidence to resolve a contrast, keep it unresolved.
Search is optional.
The main SkillAlchemy workflow handles user interaction.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 12,312 | 13,334 | +8% | 1 | 1 | 0% | 2,004 | 3,726 | +86% | 0 | 0 | — |
case-02 | pass→pass | 18,730 | 9,359 | -50% | 1 | 1 | 0% | 2,753 | 3,230 | +17% | 0 | 0 | — |
case-03 | fail→pass | 11,381 | 10,721 | -6% | 1 | 1 | 0% | 1,761 | 3,340 | +90% | 0 | 0 | — |
case-04 | pass→pass | 6,715 | 9,003 | +34% | 1 | 1 | 0% | 1,140 | 3,206 | +181% | 0 | 0 | — |
case-05 | fail→pass | 9,014 | 10,809 | +20% | 1 | 1 | 0% | 1,516 | 3,430 | +126% | 0 | 0 | — |
case-06 | pass→pass | 6,444 | 7,728 | +20% | 1 | 1 | 0% | 1,002 | 2,979 | +197% | 0 | 0 | — |
case-07 | pass→pass | 7,631 | 10,103 | +32% | 1 | 1 | 0% | 1,193 | 3,337 | +180% | 0 | 0 | — |
case-08 | pass→pass | 8,278 | 11,868 | +43% | 1 | 1 | 0% | 1,354 | 3,669 | +171% | 0 | 0 | — |
case-09 | fail→pass | 15,996 | 9,218 | -42% | 1 | 1 | 0% | 2,422 | 3,122 | +29% | 0 | 0 | — |
case-10 | fail→pass | 13,936 | 8,780 | -37% | 1 | 1 | 0% | 2,020 | 3,147 | +56% | 0 | 0 | — |
case-11 | pass→pass | 7,396 | 8,883 | +20% | 1 | 1 | 0% | 1,159 | 3,152 | +172% | 0 | 0 | — |
case-12 | fail→pass | 12,558 | 14,078 | +12% | 1 | 1 | 0% | 2,084 | 4,013 | +93% | 0 | 0 | — |
case-13 | pass→fail | 6,535 | 10,625 | +63% | 1 | 1 | 0% | 1,085 | 3,430 | +216% | 0 | 0 | — |
case-14 | pass→pass | 13,815 | 10,481 | -24% | 1 | 1 | 0% | 1,979 | 3,440 | +74% | 0 | 0 | — |
case-15 | fail→fail | 11,459 | 7,652 | -33% | 1 | 1 | 0% | 1,789 | 2,867 | +60% | 0 | 0 | — |
case-16 | fail→pass | 14,967 | 14,193 | -5% | 1 | 1 | 0% | 2,044 | 3,838 | +88% | 0 | 0 | — |
case-17 | pass→pass | 5,021 | 7,621 | +52% | 1 | 1 | 0% | 805 | 2,975 | +270% | 0 | 0 | — |
case-18 | pass→pass | 27,200 | 11,020 | -59% | 1 | 1 | 0% | 4,110 | 3,390 | -18% | 0 | 0 | — |
case-19 | pass→pass | 9,402 | 11,766 | +25% | 1 | 1 | 0% | 1,499 | 3,724 | +148% | 0 | 0 | — |
case-20 | fail→fail | 4,977 | 14,402 | +189% | 1 | 1 | 0% | 779 | 4,246 | +445% | 0 | 0 | — |
case-21 | pass→fail | 6,158 | 9,039 | +47% | 1 | 1 | 0% | 890 | 3,129 | +252% | 0 | 0 | — |
case-22 | pass→fail | 20,438 | 21,479 | +5% | 1 | 1 | 0% | 2,075 | 3,553 | +71% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +18 percentage points is the difference between those two pass rates over the 22 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.