ToolsConnector: Skill for Claude Code

.agents/skills/connector_implementer/SKILL.md

connector_implementer is a skill for Claude Code, Codex from sachinshelke/ToolsConnector. It costs 12 tokens per session (329 once invoked), scanned A, original, Apache-2.0.

Guidance for writing a connector: code that lets one application use an external tool through a shared interface. It covers authentication, tool wrappers, types, and documentation.

In plain words
What is it for?
Use it to add a service such as Gmail, Slack, or Jira to a Python tool registry, including its actions, authentication methods, and typed documentation.
Why use it?
It provides consistent rules for connecting services without writing separate authentication or integration patterns each time.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is sachinshelke/ToolsConnector's own configuration. It tells Claude Code and Codex how to work on ToolsConnector itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ToolsConnector configures →

Reuse

Borrowing it

Nothing to install: this file belongs to sachinshelke/ToolsConnector. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/sachinshelke/ToolsConnector/main/.agents/skills/connector_implementer/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/sachinshelke/ToolsConnector

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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agentmods 80×15 button for connector_implementer

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Per session 12 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 329 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00012 $0.00329
Opus 5 $0.00006 $0.00164
Sonnet 5 $0.00002 $0.00066
Haiku 4.5 $0.00001 $0.00033

Measured 8d ago against content hash f97422be58ac, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

connector_implementer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/connector_implementer/SKILL.md · 23 lines

What it actually says

Connector Implementer Persona

Overview

You act as the primary implementer of individual tools (e.g., Gmail, Slack, Jira). Your job is to construct the Python classes that wrap these tools and expose them via the unified ToolsConnector interface.

Rules

  1. Full Capability First: Do not build a "least common denominator." Expose the full power of the tool, but wrap it cleanly using the global @action decorators and standard Python types.
  2. Auth Handling: Every connector must declare its valid auth_types and leverage the core Authentication protocols. DO NOT write raw OAuth callback HTTP servers inside the connector.
  3. Use the Right Underlayer:
    • If a tier-1 official, healthy SDK exists (e.g., google-api-python-client), wrap it.
    • If no healthy SDK exists, use directly httpx or auto-generate via OpenAPI inside the connector boundary.
  4. Typing and Docstrings: You MUST type everything. Docstrings MUST clearly explain constraints, as these docstrings will literally become the LLM function calling descriptions later.

Execution Workflow

  1. When asked to create a Connector, create a directory in connectors/ with the tool's name.
  2. Separate the logic into connector.py and types.py.
  3. Add the connector to the pyproject.toml extra dependencies specifically for that tool.
  4. Review the /plan/brainstorm.md file for deeper philosophy when in doubt.
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 23 lines · 12 tokens per session scan A f97422be58ac

Subscribe to this mod's changes

connector_implementer is a skill published in the GitHub repository sachinshelke/ToolsConnector (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 12 tokens to every session and 329 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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