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955 downloads. Trust evidence available

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Agent Skills For Context Engineering

A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, o... Skill: Agent Skills For Context Engineering Owner: sanguoxxx Summary: A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, o... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-29T22:47:01.626Z | auto - Initial release of a comprehensive skill collection for context engineering in AI agent systems. - Provides st

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Rank

Safety

Downloads

955

Updated

Oct 12, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 955 downloads reported by the source. Last updated 10/12/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 12, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 12, 2026
Adoption signal: 955 downloadsadoption · observed Oct 12, 2026
Latest release: 1.0.0release · observed Apr 29, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.0.0release
- Initial release of a comprehensive skill collection for context engineering in AI agent systems. - Provides structured guidance on context management, multi-agent architectures, memory system design, and operational optimization. - Includes foundational concepts, architectural patterns, and best practices for production-grade agent development. - Offers practical activation criteria and platform-agnostic integration instructions. - Links to internal and external resources for in-depth learning and reference.
Observed Apr 29, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s175g2nywz9db4h53vhfw981d985sdbw:agent-skills-for-context-engineering

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-sanguoxxx-agent-skills-for-context-engineering/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

146,683 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

examples/book-sft-pipeline/SKILL.md

---
name: book-sft-pipeline
description: This skill should be used when the user asks to "fine-tune on books", "create SFT dataset", "train style model", "extract ePub text", or mentions style transfer, LoRA training, book segmentation, or author voice replication.
version: 2.0.0
---

# Book SFT Pipeline

A complete system for converting books into SFT datasets and training style-transfer models. This skill teaches the pipeline from raw ePub to a model that writes in any author's voice.

## When to Activate

Activate this skill when:
- Building fine-tuning datasets from literary works
- Creating author-voice or style-transfer models
- Preparing training data for Tinker or similar SFT platforms
- Designing text segmentation pipelines for long-form content
- Training small models (8B or less) on limited data

## Core Concepts

### The Three Pillars of Book SFT

**1. Intelligent Segmentation**
Text chunks must be semantically coherent. Breaking mid-sentence teaches the model to produce fragmented output. Target: 150-400 words per chunk, always at natural boundaries.

**2. Diverse Instruction Generation**
Use multiple prompt templates and system prompts to prevent overfitting. A single prompt style leads to memorization. Use 15+ prompt templates with 5+ system prompts.

**3. Style Over Content**
The goal is learning the author's rhythm and vocabulary patterns, not memorizing plots. Synthetic instructions describe what happens without quoting the text.

## Pipeline Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│                    ORCHESTRATOR AGENT                           │
│  Coordinates pipeline phases, manages state, handles failures   │
└──────────────────────┬──────────────────────────────────────────┘
                       │
       ┌───────────────┼───────────────┬───────────────┐
       ▼               ▼               ▼               ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│  EXTRACTION  │ │ SEGMENTATION │ │  INSTRUCTION │ │   DATASET    │
│    AGENT     │ │    AGENT     │ │    AGENT     │ │   BUILDER    │
│ ePub → Text  │ │ Text → Chunks│ │ Chunks →     │ │ Pairs →      │
│              │ │ 150-400 words│ │ Prompts      │ │ JSONL        │
└──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘
                       │
       ┌───────────────┴───────────────┐
       ▼                               ▼
┌──────────────┐               ┌──────────────┐
│   TRAINING   │               │  VALIDATION  │
│    AGENT     │               │    AGENT     │
│ LoRA on      │               │ AI detector  │
│ Tinker       │               │ Originality  │
└──────────────┘               └──────────────┘
```

## Phase 1: Text Extraction

### Critical Rules
1. **Always source ePub over PDF** - OCR errors become learned patterns
2. **Use paragraph-level extraction** - Extract from `<p>` tags to preserve breaks
3. **Remove front/back matter** - Copyright and TOC pollute the dataset

``

examples/digital-brain-skill/SKILL.md

---
name: digital-brain
description: This skill should be used when the user asks to "write a post", "check my voice", "look up contact", "prepare for meeting", "weekly review", "track goals", or mentions personal brand, content creation, network management, or voice consistency.
version: 1.0.0
---

# Digital Brain

A structured personal operating system for managing digital presence, knowledge, relationships, and goals with AI assistance. Designed for founders building in public, content creators growing their audience, and tech-savvy professionals seeking AI-assisted personal management.

**Important**: This skill uses progressive disclosure. Module-specific instructions are in each subdirectory's `.md` file. Only load what's needed for the current task.

## When to Activate

Activate this skill when the user:

- Requests content creation (posts, threads, newsletters) - load identity/voice.md first
- Asks for help with personal brand or positioning
- Needs to look up or manage contacts/relationships
- Wants to capture or develop content ideas
- Requests meeting preparation or follow-up
- Asks for weekly reviews or goal tracking
- Needs to save or retrieve bookmarked resources
- Wants to organize research or learning materials

**Trigger phrases**: "write a post", "my voice", "content ideas", "who is [name]", "prepare for meeting", "weekly review", "save this", "my goals"

## Core Concepts

### Progressive Disclosure Architecture

The Digital Brain follows a three-level loading pattern:

| Level | When Loaded | Content |
|-------|-------------|---------|
| **L1: Metadata** | Always | This SKILL.md overview |
| **L2: Module Instructions** | On-demand | `[module]/[MODULE].md` files |
| **L3: Data Files** | As-needed | `.jsonl`, `.yaml`, `.md` data |

### File Format Strategy

Formats chosen for optimal agent parsing:

- **JSONL** (`.jsonl`): Append-only logs - ideas, posts, contacts, interactions
- **YAML** (`.yaml`): Structured configs - goals, values, circles
- **Markdown** (`.md`): Narrative content - voice, brand, calendar, todos
- **XML** (`.xml`): Complex prompts - content generation templates

### Append-Only Data Integrity

JSONL files are **append-only**. Never delete entries:
- Mark as `"status": "archived"` instead of deleting
- Preserves history for pattern analysis
- Enables "what worked" retrospectives

## Detailed Topics

### Module Overview

```
digital-brain/
├── identity/     → Voice, brand, values (READ FIRST for content)
├── content/      → Ideas, drafts, posts, calendar
├── knowledge/    → Bookmarks, research, learning
├── network/      → Contacts, interactions, intros
├── operations/   → Todos, goals, meetings, metrics
└── agents/       → Automation scripts
```

### Identity Module (Critical for Content)

**Always read `identity/voice.md` before generating any content.**

Contains:
- `voice.md` - Tone, style, vocabulary, patterns
- `brand.md` - Positioning, audience, content pillars
- `values.yaml` - Core beliefs and principl

examples/interleaved-thinking/generated_skills/comprehensive-research-agent/SKILL.md

---
name: comprehensive-research-agent
description: "Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls"
---

# Comprehensive Research Agent Best Practices

This skill addresses common failures in multi-step research tasks: unhandled tool errors, missing validation, opaque reasoning, and premature conclusions. It provides structured protocols for source validation, error recovery, and thinking transparency that significantly improves research quality and reliability.

## When to Activate

- Task involves web research with search, read_url, or fetch operations
- Task requires gathering information from multiple sources
- Task has explicit requirements for completeness or verification
- Task includes file operations that need validation (save, write, read)
- Any research or information-gathering workflow with 3+ tool interactions

## Core Concepts

- Validation Checkpoints: Explicit verification steps at phase transitions to confirm tool outputs, source relevance, and information completeness before proceeding
- Error Recovery Protocols: Mandatory acknowledgment and handling of tool failures with fallback strategies rather than silent continuation
- Source Traceability: Maintaining clear tracking of which sources were actually retrieved vs. referenced from prior knowledge to prevent hallucination
- Substantive Thinking Blocks: Detailed reasoning traces that document insights, connections, gaps, and decision rationale at each step
- Cross-Source Validation: Verifying key claims against multiple sources and explicitly noting consensus, contradictions, and information gaps

## Patterns to Avoid

- *Silent Tool Failure**: A tool call returns an error (404, timeout, invalid URL) but the agent proceeds without acknowledging it, potentially missing critical information. Always log failures and attempt recovery or document the gap.
- *Vague Completion Claims**: Agent declares 'I have enough information' or 'research is comprehensive' without specifying what was learned, what sources support claims, or what gaps remain. Replace with specific summaries of coverage.
- *Unvalidated Source Selection**: Agent reads URLs from search results without evaluating relevance, credibility, or recency first. This wastes tool calls on low-quality sources. Always rank and prioritize sources before deep reading.
- *Generic Thinking Blocks**: Thinking contains only next-action descriptions ('Now I will search for X') without analysis of what was learned, how it connects to the goal, or what questions remain. Thinking should be substantive and reflective.
- *Verification Method Error**: Using list_directory to verify file creation can produce false negatives due to caching. Always use read_file for actual content verification.
- *Citation Without Retrieval**: Citing sources (URLs, paper titles) in the final report that were never successfully fetched or read. Track sources explicitly and prohibit citing unretrieved

examples/interleaved-thinking/SKILL.md

---
name: reasoning-trace-optimizer
description: "Debug and optimize AI agents by analyzing reasoning traces. Activates on 'debug agent', 'optimize prompt', 'analyze reasoning', 'why did the agent fail', 'improve agent performance', or when diagnosing agent failures and context degradation."
---

# Reasoning Trace Optimizer

Debug and optimize AI agents by analyzing their reasoning traces. This skill uses MiniMax M2.1's interleaved thinking to provide deep insight into agent decision-making and generate concrete improvements.

## When to Activate

- User asks to "debug agent", "analyze reasoning", or "optimize prompt"
- Agent task fails and user wants to understand why
- User mentions "context degradation", "tool confusion", or "instruction drift"
- Request to improve agent performance or reduce errors
- User wants to generate shareable learnings from debugging sessions
- After repeated failures on similar tasks

## Core Concepts

### Interleaved Thinking

Unlike standard reasoning models that think once at the start, interleaved thinking allows reasoning BETWEEN each tool interaction. This is critical because:

1. **Long-horizon tasks** require maintaining focus across many turns
2. **External perturbations** (tool outputs, environment changes) need real-time adaptation
3. **Debugging** requires seeing HOW decisions were made, not just WHAT was output

### The Optimization Loop

```
Execute Agent → Capture Traces → Analyze Patterns → Optimize Prompt → Re-run
                                                          ↑____________|
```

Each iteration improves the prompt based on detected patterns until convergence.

### Pattern Detection

Common failure patterns the analyzer detects:

| Pattern | Description |
|---------|-------------|
| `context_degradation` | Model loses track of information over long contexts |
| `tool_confusion` | Model misunderstands tool capabilities or outputs |
| `instruction_drift` | Model gradually deviates from original instructions |
| `goal_abandonment` | Model stops pursuing the original goal |
| `circular_reasoning` | Model repeats similar actions without progress |
| `premature_conclusion` | Model concludes before completing the task |

## Usage Modes

### Mode 1: M2.1 Agent Debugging

Run a task through M2.1 and analyze its reasoning:

```python
from reasoning_trace_optimizer import TraceCapture, TraceAnalyzer

capture = TraceCapture()
trace = capture.run(
    task="Search for Python tutorials and summarize them",
    system_prompt="You are a research assistant.",
    tools=[search_tool],
    tool_executor=execute_search
)

analyzer = TraceAnalyzer()
analysis = analyzer.analyze(trace)

print(f"Score: {analysis.overall_score}/100")
for pattern in analysis.patterns:
    print(f"Found: {pattern.type.value} - {pattern.suggestion}")
```

### Mode 2: Full Optimization Loop

Automatically iterate until the prompt is optimized:

```python
from reasoning_trace_optimizer import OptimizationLoop, LoopConfig

config = LoopCo

SKILL.md

---
name: context-engineering-collection
description: A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.
---

# Agent Skills for Context Engineering

This collection provides structured guidance for building production-grade AI agent systems through effective context engineering.

## When to Activate

Activate these skills when:
- Building new agent systems from scratch
- Optimizing existing agent performance
- Debugging context-related failures
- Designing multi-agent architectures
- Creating or evaluating tools for agents
- Implementing memory and persistence layers

## Skill Map

### Foundational Context Engineering

**Understanding Context Fundamentals**
Context is not just prompt text—it is the complete state available to the language model at inference time, including system instructions, tool definitions, retrieved documents, message history, and tool outputs. Effective context engineering means understanding what information truly matters for the task at hand and curating that information for maximum signal-to-noise ratio.

**Recognizing Context Degradation**
Language models exhibit predictable degradation patterns as context grows: the "lost-in-middle" phenomenon where information in the center of context receives less attention; U-shaped attention curves that prioritize beginning and end; context poisoning when errors compound; and context distraction when irrelevant information overwhelms relevant content.

### Architectural Patterns

**Multi-Agent Coordination**
Production multi-agent systems converge on three dominant patterns: supervisor/orchestrator architectures with centralized control, peer-to-peer swarm architectures for flexible handoffs, and hierarchical structures for complex task decomposition. The critical insight is that sub-agents exist primarily to isolate context rather than to simulate organizational roles.

**Memory System Design**
Memory architectures range from simple scratchpads to sophisticated temporal knowledge graphs. Vector RAG provides semantic retrieval but loses relationship information. Knowledge graphs preserve structure but require more engineering investment. The file-system-as-memory pattern enables just-in-time context loading without stuffing context windows.

**Filesystem-Based Context**
The filesystem provides a single interface for storing, retrieving, and updating effectively unlimited context. Key patterns include scratch pads for tool output offloading, plan persistence for long-horizon tasks, sub-agent communication via shared files, and dynamic skill loading. Agents use `ls`, `glob`, `grep`, and `read_file` for targeted context discovery, often outperforming semantic search for structural queries.

**Hosted Agent Infrastructure**
Background coding agents run in remote sandboxed environments rather than on local machines

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