cachibot-plugin
Skill19 starsChanged 9 months agoMITView on GitHub
Create new CachiBot agent plugins (tools) following the project's capability-gated plugin architecture. Use this skill when adding a new tool, capability, or plugin to the CachiBot agent system — e.g., "add a translate tool", "create a web scraping plugin", "add a new capability".
In this repository9 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- cachibot-api-routeSkill
- cachibot-frontend-viewSkill
- cachibot-full-stack-entitySkill
- cachibot-websocket-eventSkill
- code-reviewSkill
- create-prSkill
- lint-fixSkill
- review-fixSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 11
Create new plugins that expose tools to the CachiBot agent.
- Very specific: line 215
If the tool uses an AI model (image gen, audio gen, etc.), read from ctx.bot_models:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
CachiBot Plugin Creation
Create new plugins that expose tools to the CachiBot agent. Plugins are capability-gated, meaning they're only loaded when a bot has the corresponding capability enabled.
Architecture Overview
- Base class:
CachibotPluginextends Tukuy'sTransformerPlugin - Context:
PluginContextcarries config, sandbox, bot_id, tool_configs, bot_models - Skills: Each plugin exposes tools via Tukuy's
@skilldecorator - Registry:
PluginManagerbridges plugin skills to Prompture'sToolRegistry
Step-by-Step Process
1. Create the Plugin File
Create cachibot/plugins/<your_plugin>.py:
"""
<Description> plugin — <tool_name> tool.
<What it does and what external services it uses.>
"""
import logging
from tukuy.manifest import PluginManifest, PluginRequirements
from tukuy.skill import ConfigParam, RiskLevel, Skill, skill
from cachibot.plugins.base import CachibotPlugin, PluginContext
logger = logging.getLogger(__name__)
class YourPlugin(CachibotPlugin):
"""Provides the <tool_name> tool for <purpose>."""
def __init__(self, ctx: PluginContext) -> None:
super().__init__("<plugin_name>", ctx)
self._skills_map = self._build_skills()
@property
def manifest(self) -> PluginManifest:
return PluginManifest(
name="<plugin_name>",
display_name="<Display Name>",
icon="<lucide-icon-name>",
group="<group>", # e.g. "Creative", "Utility", "Integration"
requires=PluginRequirements(network=True), # set True if network needed
)
def _build_skills(self) -> dict[str, Skill]:
ctx = self.ctx
@skill(
name="<tool_name>",
description="<Clear description of what the tool does for the LLM>",
category="<category>",
tags=["<tag1>", "<tag2>"],
side_effects=False, # True if it modifies external state
requires_network=True, # True if it makes network calls
display_name="<Display Name>",
icon="<icon>",
risk_level=RiskLevel.MODERATE, # SAFE, MODERATE, DANGEROUS, CRITICAL
config_params=[
ConfigParam(
name="<param_name>",
display_name="<Param Display Name>",
description="<What this config does>",
type="<type>", # "string", "number", "select", "boolean"
default=<default_value>,
# For select: options=["opt1", "opt2"]
# For number: min=0, max=100, step=1, unit="seconds"
),
],
)
async def your_tool(arg1: str, arg2: str = "") -> str:
"""Tool function docstring (shown in API docs).
Args:
arg1: Description of arg1.
arg2: Description of arg2. Defaults to plugin config.
Returns:
Human-readable result string.
"""
# Access per-tool config
tool_cfg = ctx.tool_configs.get("<tool_name>", {})
effective_arg2 = arg2 or tool_cfg.get("<param_name>", "<default>")
# Access bot model slots (if tool uses a specific model)
model = ""
if ctx.bot_models:
model = ctx.bot_models.get("<slot>", "") # "image", "audio", etc.
try:
# Implementation here
result = "..."
return result
except Exception as exc:
logger.error("<tool_name> failed: %s", exc, exc_info=True)
return f"Error: <tool_name> failed: {exc}"
return {"<tool_name>": your_tool.__skill__}
@property
def skills(self) -> dict[str, Skill]:
return self._skills_map2. Export from __init__.py
Add to cachibot/plugins/__init__.py:
from cachibot.plugins.your_plugin import YourPlugin
# Add to CACHIBOT_PLUGINS dict:
CACHIBOT_PLUGINS: dict[...] = {
# ... existing entries ...
"your_plugin": YourPlugin,
}
# Add to __all__:
__all__ = [
# ... existing entries ...
"YourPlugin",
]3. Register in Plugin Manager
Edit cachibot/services/plugin_manager.py:
Option A — Gate under a NEW capability:
from cachibot.plugins import YourPlugin
CAPABILITY_PLUGINS: dict[str, list[PluginClass]] = {
# ... existing mappings ...
"yourCapability": [YourPlugin],
}Then add the capability to the frontend types (Step 5).
Option B — Gate under an EXISTING capability:
CAPABILITY_PLUGINS: dict[str, list[PluginClass]] = {
"webAccess": [WebPlugin, HttpPlugin, YourPlugin], # append to existing
}Option C — Always enabled (no capability gate):
ALWAYS_ENABLED: list[PluginClass] = [TaskPlugin, NotesPlugin, YourPlugin]4. Add Frontend Capability (if new capability)
Edit frontend/src/types/index.ts — add to BotCapabilities:
export interface BotCapabilities {
// ... existing capabilities ...
yourCapability: boolean
}5. Add Frontend Capability Toggle (if new capability)
In the bot settings view (frontend/src/components/views/SettingsView.tsx), add a toggle for the new capability so users can enable/disable it per bot.
Key Patterns
Returning Media
For tools that produce images or audio, return markdown data URIs:
# Image
return f""
# Audio
return f""The frontend's MarkdownRenderer auto-detects these and renders <img> or <audio> elements.
Config Params
Config params defined in @skill(config_params=[...]) become per-tool settings in the bot's toolConfigs. Access them via:
cfg = ctx.tool_configs.get("tool_name", {})
value = cfg.get("param_name", default_value)Risk Levels
SAFE— No side effects, read-onlyMODERATE— External API calls, generates contentDANGEROUS— Writes files, executes code, sends messagesCRITICAL— Destructive operations, shell access
Bot Model Slots
If the tool uses an AI model (image gen, audio gen, etc.), read from ctx.bot_models:
model = ctx.bot_models.get("image", "") if ctx.bot_models else ""Available slots: default, image, audio, structured.
Checklist
- Plugin file created in
cachibot/plugins/ - Plugin extends
CachibotPluginwith proper__init__,manifest,_build_skills,skills - Skills use
@skilldecorator with description, risk_level, config_params - Plugin exported in
cachibot/plugins/__init__.py - Plugin registered in
cachibot/services/plugin_manager.py - If new capability: added to
BotCapabilitiesin frontend types - If new capability: toggle added to settings view
- Tool returns human-readable strings (not raw JSON)
- Errors caught and returned as
"Error: ..."strings (never raise)
Does it work?
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- It worked
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
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