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permanu

SuperSkill

by permanu
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TypeScript
Local

capture

Destructive

Save multiple insights from a conversation as individual vault items. Each gets its own auto-numbered file and supports any content type.

Instructions

Batch-capture multiple insights from a conversation into individual vault items. Each item gets its own file with auto-numbering. Supports any content type (learning, decision, adr, prd, research, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesItems to capture from the conversation
projectNoProject slug (auto-detected if omitted)
workspace_pathNoAbsolute thread workspace directory; must map to this project. Omit only when the server cwd is the thread workspace.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.15.1
    • addedInput schema / properties / items / items / additionalProperties
      Added value: +false
    • addedInput schema / properties / workspace_path
      Added value: +{
+  "description": "Absolute thread workspace directory; must map to this project. Omit only when the server cwd is the thread workspace.",
+  "type": "string"
+}
  2. First observedv0.6.1

TDQS

B3.4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already flag destructiveHint=true. The description adds real value beyond that by disclosing per-item file creation and auto-numbering, which tells the agent this fans out into many artifacts. It does not say whether items overwrite existing files, what happens on partial failure, or what is returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three compact sentences with the core action front-loaded and no filler. Slightly more could be trimmed by folding the content-type list, but every sentence carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 100% schema coverage and no output schema, the description covers the action and its file-per-item effect. It is still thin for a destructive-flagged batch mutation: no return shape, no failure or duplicate-handling behavior, and no note on the workspace_path/project coupling described in the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter (items, project, workspace_path, and nested item fields) is already documented in the schema. The description only restates the content-type flexibility, adding no syntax, defaults, or constraints beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Batch-capture multiple insights from a conversation into individual vault items.' The batching and one-file-per-item outcome are clear. However, it never distinguishes itself from close siblings like learn, write, extract, or decide, so an agent must infer which one applies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'From a conversation' and 'batch' imply the usage context, and the enumerated content types signal breadth. But there is no explicit when-to-use guidance versus learn/write/decide, and no exclusions or prerequisites (e.g. when not to batch).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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