| name | lens |
| description | Long-context analysis — examine a large document or codebase under a focused lens using active retrieval over Kimi's 256K window, surfacing insights, relations, and inconsistencies |
| user-invocable | true |
Lens - Long-Context Analysis
Deeply analyze a large document, codebase, or transcript that's too big to reason about by skimming. Uses ACTIVE RETRIEVAL (map → query → synthesize) over Kimi K2.6's 256K window instead of dumping everything in at once — which avoids the "lost in the middle" failure mode of naive long-context prompting.
Usage
/lens [target + what you want to know]
/lens [files...] [question]
When to Use
- A document/codebase/log is too large to hold in your head
- You need specific insights, not a generic summary
- You suspect inconsistencies or hidden relationships across sections
- Naive "read it all" would blow context or miss the middle
/lens vs alternatives
| /lens | /decompose | gemini_analyze_text |
|---|
| Input | Large existing content (≤256K) | A problem to split | A single argument/text |
| Strategy | Map → query sections → synthesize | Split into sub-problems | Rhetoric/bias dissection |
| Use when | "Understand this big thing" | "Break this problem down" | "Analyze this claim" |
Instructions
When user invokes /lens [target]:
Step 1: Map the structure first (don't dump)
Identify the target (file paths or pasted content). Get a structural map BEFORE deep analysis:
mcp__tachibot-mcp__kimi_long_context({
content: "[paste content here, OR a one-line note if using files below]",
files: ["path/to/file1", "path/to/dir/**"],
task: "summarize",
outputFormat: "structured",
query: "Map the structure: list the major sections/modules/themes and what each covers. Do NOT analyze yet — just produce the table of contents."
})
Present the map to the user: "Found [N] sections: ... Diving into the relevant ones."
Step 2: Targeted analysis (active retrieval)
For the user's actual question, query specific aspects rather than re-reading everything:
mcp__tachibot-mcp__kimi_long_context({
content: "[content]",
files: ["..."],
task: "analyze",
outputFormat: "detailed",
query: "[the user's specific question, scoped to the relevant sections found in Step 1]"
})
Use task: "extract" to pull specific facts, "find" to locate something, "compare" to contrast sections.
Step 3: Cross-check for inconsistencies (if the content makes claims)
If the content argues or claims things, get an independent second read on the key parts:
mcp__tachibot-mcp__gemini_analyze_text({
text: "[the key claims/sections surfaced in Step 2]",
type: "key-points"
})
Step 4: Synthesize
mcp__tachibot-mcp__nextThought({
thought: "Synthesize the long-context analysis of [target].\n\nFindings:\n[Step 2 + Step 3 results]\n\nProduce:\n1. Direct answer to the user's question\n2. Key insights (ranked)\n3. Relations/dependencies across sections\n4. Inconsistencies or gaps found\n5. What to look at next",
model: "gemini",
executeModel: true,
contextWindow: "all",
nextThoughtNeeded: false
})
If kimi_long_context is unavailable, fall back to gemini_analyze_text (1M context) using the files parameter for the analysis steps.
Step 5: Present Results
## Lens: [target]
### Answer
[Direct answer to the question]
### Key Insights
1. [ranked insight]
...
### Relations / Dependencies
[How sections connect — what depends on what]
### Inconsistencies & Gaps
[Contradictions or missing pieces found]
### Look At Next
[Where to dig further]
Examples
/lens src/**/*.ts how does auth flow through this codebase?/lens [paste 80-page spec] what contradictions exist between sections 3 and 7?/lens server.log find the root-cause chain for the 02:14 outage/lens compare these two RFCs and tell me where they disagree
