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cachly-dev

Cachly — AI Cognitive Brain

cls_install_hooks

Generates git hooks and CI steps that send learning signals to your Cachly brain after each commit or CI run.

Instructions

READ-ONLY — outputs text only, writes no files, makes no network calls, has no side effects. Generates ready-to-paste shell scripts: a git post-commit hook and/or a GitHub Actions step. You must manually copy and install the output. Once the generated scripts are installed, each git commit or CI run will make outbound HTTPS calls to api.cachly.dev to feed learning signals to your brain. No auth required to call this tool — only an instance_id. Run once per repository.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesBrain instance ID
repo_pathNoPath to repo root (default: current dir)
hooksNoWhich hooks to output (default: ["git", "ci"])
Behavior5/5

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

The description clearly declares the tool as READ-ONLY, with no side effects: outputs text only, writes no files, makes no network calls. It also explains the downstream effect of the generated scripts (HTTPS calls). This fully discloses behavior without annotations.

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

Conciseness5/5

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

The description is concise, with key information front-loaded (READ-ONLY, outputs text only). Each sentence adds necessary detail without fluff. It is well-structured and easy to parse.

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

Completeness5/5

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

For a simple script generation tool with no output schema, the description adequately covers what the tool does, how to use it, and the consequences. It addresses prerequisites (instance_id), output format, and post-installation behavior. No gaps are evident.

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

Parameters4/5

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

The input schema has 100% description coverage, so baseline is 3. The description adds value by explaining the purpose of the output (shell scripts) and the manual installation step, which complements the schema. It does not repeat schema descriptions but provides contextual usage guidance.

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

Purpose5/5

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

The description clearly states the tool generates shell scripts for git post-commit hooks and/or GitHub Actions steps. It specifies the output type and purpose, distinguishing it from other tools in the sibling list that perform learning, caching, or other operations.

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

Usage Guidelines4/5

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

The description provides explicit instructions: 'You must manually copy and install the output' and 'Run once per repository.' It also notes that no auth is required, guiding when to use the tool. However, it does not explicitly contrast with alternative tools or state when not to use it.

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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