index

A catalogue of reusable AI agents, each with defined instructions and capabilities. It includes agents for evaluating responses, researching information, and coordinating tasks.

In plain words
What is it for?
Use it to assess AI-generated answers, compare responses, create evaluation rubrics, search and verify information, extract web content, and combine research from multiple sources.
Why use it?
It helps you find a suitable agent for a specific job without designing one from scratch. The descriptions also show what each agent is intended to handle.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/muratcankoylan/agent-skills-for-context-engineering/index
Clone the repo
git clone --depth 1 https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 597 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00000 $0.00597
Opus 5 $0.00000 $0.00298
Sonnet 5 $0.00000 $0.00119
Haiku 4.5 $0.00000 $0.00060

Measured yesterday against content hash 8c85022761a0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

index 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 yesterday.

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.

examples/llm-as-judge-skills/agents/index.md · 115 lines

How it starts

The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agents Index

Agents are reusable AI components with defined capabilities, tools, and instructions.

Available Agents

Evaluator Agent

Path: agents/evaluator-agent/evaluator-agent.md Purpose: Assess the quality of LLM-generated responses

Capabilities:

  • Direct scoring against rubrics
  • Pairwise comparison of responses
  • Criteria extraction from task descriptions
  • Rubric generation for evaluation

Tools Used:

  • directScore
  • pairwiseCompare
  • extractCriteria
  • generateRubric

Best For:

  • Quality gates in content pipelines
  • Model comparison studies
  • RLHF preference data generation
  • Output validation before delivery

Research Agent

Path: agents/research-agent/research-agent.md Purpose: Gather, verify, and synthesize information from multiple sources

Capabilities:

  • Web search and result analysis
  • URL content extraction
  • Claim extraction and verification
  • Research synthesis

Tools Used:

  • webSearch
  • readUrl
  • extractClaims
  • verifyClaim
  • synthesize

Best For:

  • Knowledge base building
  • Fact checking
  • Market research
  • Technical documentation

Orchestrator Agent

Path: agents/orchestrator-agent/orchestrator-agent.md Purpose: Coordinate multi-agent workflows for complex tasks

Capabilities:

  • Task decomposition and assignment
  • Parallel task execution
  • Result synthesis
  • Error handling and recovery

Tools Used:

  • delegateToAgent
  • parallelExecution
  • waitForCompletion
  • synthesizeResults
  • handleError

Best For:

  • Complex multi-step tasks
  • Cross-capability workflows
  • Quality-assured pipelines
  • Long-running operations

Agent Interaction Patterns

Sequential Pipeline

Input → Agent A → Agent B → Agent C → Output

Use when each step depends on the previous.

Parallel Fan-Out

        ┌→ Agent A ─┐
Input ──┼→ Agent B ──┼→ Synthesis → Output
        └→ Agent C ─┘

Use for independent subtasks that can run concurrently.

Read the full file on GitHub · 115 lines

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. yesterday First seen · 115 lines · 0 tokens per session scan A 8c85022761a0

Subscribe to this mod's changes

index is an agent published in the GitHub repository muratcankoylan/Agent-Skills-for-Context-Engineering (17,868 stars, last pushed 13d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 597 tokens. 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 2026-08-30.

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