SKU: 40359753557

COMP Cams Camshaft Kit FS Nostalgia 271 SK31-670-4

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Description

COMP Cams Camshaft Kit FS Nostalgia 271 SK31-670-4COMP Cams Camshafts SK31 670 4 CamShaft Kit, FS Nostalgia 271 H+ SK31 670 4 CamShaft Kit, FS Nostalgia 271 H+ COMP Cams Camshaft Kit FS Nostalgia 271 Vehicle Fitments: Year Make Model Submodel 1963 Ford 300 Base 1966 1974 Ford Bronco Base 1975 1993 Ford Bronco Custom 1985 1993 Ford Bronco Eddie Bauer 1975 Ford Bronco Northland, Ranger 1980 1981 Ford Bronco Ranger XLT 1968 1969 Ford Bronco Roadster 1975 1977 Ford Bronco Sport 1968 1973 Ford Bronco

COMP Cams Camshafts SK31-670-4 CamShaft Kit, FS Nostalgia 271 H+ SK31-670-4 - CamShaft Kit, FS Nostalgia 271 H+ COMP Cams Camshaft Kit FS Nostalgia 271

Vehicle Fitments:

Year Make Model Submodel
1963 Ford 300 Base
1966 - 1974 Ford Bronco Base
1975 - 1993 Ford Bronco Custom
1985 - 1993 Ford Bronco Eddie Bauer
1975 Ford Bronco Northland, Ranger
1980 - 1981 Ford Bronco Ranger XLT
1968 - 1969 Ford Bronco Roadster
1975 - 1977 Ford Bronco Sport
1968 - 1973 Ford Bronco Wagon
1990 - 1993 Ford Bronco XL
1982 - 1983 Ford Bronco XLS
1984 - 1992 Ford Bronco XLT
1982 - 1993 Ford Bronco XLT Lariat
1992 Ford Bronco XLT Nite
1963 - 1972 Ford Country Sedan Base
1963 - 1972 Ford Country Squire Base
1964 - 1972 Ford Custom Base
1964 - 1972 Ford Custom 500 Base
1969 - 1982 Ford E-100 Econoline Base
1975 - 1982 Ford E-100 Econoline Chateau
1969 - 1974 Ford E-100 Econoline Club Wagon
1975 - 1983 Ford E-100 Econoline Custom
1975 Ford E-100 Econoline Northland
1983 Ford E-100 Econoline XL
1979 - 1982 Ford E-100 Econoline Club Wagon Chateau, Base
1979 - 1983 Ford E-100 Econoline Club Wagon Custom
1983 Ford E-100 Econoline Club Wagon XL
1975 - 1993 Ford E-150 Econoline Base
1975 - 1982 Ford E-150 Econoline Chateau
1975 - 1991 Ford E-150 Econoline Custom
1975 Ford E-150 Econoline Northland
1983 - 1993 Ford E-150 Econoline XL
1979 - 1986 Ford E-150 Econoline Club Wagon Base
1979 - 1993 Ford E-150 Econoline Club Wagon Chateau, Custom
1983 - 1991 Ford E-150 Econoline Club Wagon XL
1984 - 1993 Ford E-150 Econoline Club Wagon XLT
1969 - 1974 Ford E-200 Econoline Base, Club Wagon
1971 - 1972 Ford E-200 Econoline Chateau Wagon
1970 - 1974 Ford E-200 Econoline Custom Wagon
1975 - 1986 Ford E-250 Econoline Base
1975 - 1982 Ford E-250 Econoline Chateau
1975 - 1991 Ford E-250 Econoline Custom
1975 Ford E-250 Econoline Northland
1983 - 1991 Ford E-250 Econoline XL
1979 - 1980 Ford E-250 Econoline Club Wagon Chateau, Base, Custom
1970 - 1974 Ford E-300 Econoline Chateau Wagon
1969 - 1974 Ford E-300 Econoline Club Wagon, Base
1971 - 1972 Ford E-300 Econoline Custom Wagon
1975 - 1982 Ford E-350 Econoline Custom, Base, Chateau
1975 Ford E-350 Econoline Northland
1979 - 1980 Ford E-350 Econoline Club Wagon Chateau, Custom, Base
1969 - 1983 Ford F-100 Base
1975 - 1982 Ford F-100 Custom
1975 - 1978 Ford F-100 Northland
1978 - 1981 Ford F-100 Ranger Lariat
1975 - 1981 Ford F-100 Ranger XLT, Ranger
1982 - 1983 Ford F-100 XLS, XL, XLT Lariat
1977 Ford F-100 XLT
1976 - 1986 Ford F-150 Base
1975 - 1992 Ford F-150 Custom
1993 Ford F-150 Lightning
1975 - 1978 Ford F-150 Northland
1975 - 1989 Ford F-150 Ranger
1978 - 1981 Ford F-150 Ranger Lariat
1975 - 1981 Ford F-150 Ranger XLT
1982 - 1993 Ford F-150 XL
1982 - 1983 Ford F-150 XLS
1977 - 1993 Ford F-150 XLT
1982 - 1992 Ford F-150 XLT Lariat
1976 - 1986 Ford F-250 Base
1975 - 1993 Ford F-250 Custom
1975 - 1978 Ford F-250 Northland
1975 - 1992 Ford F-250 Ranger
1978 - 1981 Ford F-250 Ranger Lariat
1975 - 1981 Ford F-250 Ranger XLT
1982 - 1993 Ford F-250 XL
1982 - 1983 Ford F-250 XLS
1977 - 1993 Ford F-250 XLT
1982 - 1992 Ford F-250 XLT Lariat
1975 - 1980 Ford F-350 Custom, Ranger, Base, Ranger XLT
1975 - 1978 Ford F-350 Northland
1978 - 1980 Ford F-350 Ranger Lariat
1977 Ford F-350 XLT
1963 - 1970 Ford Fairlane 500
1966 - 1967 Ford Fairlane 500XL
1963 - 1969 Ford Fairlane Base
1978 - 1979 Ford Fairmont Base, Futura
1979 Ford Fairmont Elite, Wagon
1963 - 1965 Ford Falcon Futura Sprint
1963 - 1970 Ford Falcon Futura, Base
1963 - 1965 Ford Falcon Sedan Delivery Base
1963 - 1967 Ford Galaxie Base
1963 - 1972 Ford Galaxie 500 Base
1963 - 1964 Ford Galaxie 500 Victoria, Sunliner
1963 - 1970 Ford Galaxie 500 XL
1972 - 1974 Ford Gran Torino Base, Squire, Sport
1973 - 1974 Ford Gran Torino Brougham
1974 Ford Gran Torino Elite
1978 - 1980 Ford Granada ESS
1975 - 1980 Ford Granada Ghia, Base
1965 - 1981 Ford LTD Base
1970 - 1972 Ford LTD Brougham
1977 - 1981 Ford LTD Country Squire
1980 - 1981 Ford LTD Crown Victoria, S
1977 - 1979 Ford LTD Landau
1977 - 1979 Ford LTD II Base, S
1977 - 1978 Ford LTD II Brougham
1979 Ford LTD II Landau
1977 Ford LTD II Squire
1971 - 1977 Ford Maverick Base
1975 - 1977 Ford Maverick Grabber
1964 - 1979 Ford Mustang Base
1969 - 1971 Ford Mustang Boss 302
1971 - 1972 Ford Mustang Boss 351
1969 - 1970 Ford Mustang Boss 429
1979 Ford Mustang Ghia
1970 - 1973 Ford Mustang Mach 1, Grande
1965 - 1970 Ford Mustang Shelby GT-350
1966 Ford Mustang Shelby GT-350H
1967 - 1970 Ford Mustang Shelby GT-500
1968 Ford Mustang Shelby GT-500KR
1975 - 1978 Ford Mustang II Ghia, Base, Mach 1
1963 - 1972 Ford Ranch Wagon Base
1970 Ford Ranch Wagon Police Cruiser
1967 - 1979 Ford Ranchero 500
1967 Ford Ranchero 500 XL
1963 - 1971 Ford Ranchero Base
1966 Ford Ranchero Custom
1968 - 1979 Ford Ranchero GT
1970 - 1979 Ford Ranchero Squire
1963 - 1964 Ford Sprint Base
1977 - 1981 Ford Thunderbird Base
1978 Ford Thunderbird Diamond Jubilee
1979 - 1981 Ford Thunderbird Heritage
1980 Ford Thunderbird Silver Anniversary
1978 - 1981 Ford Thunderbird Town Landau
1971 Ford Torino 500
1968 - 1974 Ford Torino Base
1970 - 1971 Ford Torino Brougham, Super Cobra Jet
1968 - 1971 Ford Torino GT
1969 - 1971 Ford Torino Squire, Cobra
1980 Lincoln Continental Base
1977 - 1980 Lincoln Versailles Base
1964 - 1967 Mercury Caliente Base
1979 Mercury Capri Ghia, Base
1964 - 1965 Mercury Comet 404, 202
1963 - 1977 Mercury Comet Base
1963 Mercury Comet Custom, S-22
1967 Mercury Commuter Base
1967 - 1981 Mercury Cougar Base, XR-7
1969 - 1970 Mercury Cougar Boss 429, Cobra Jet, Boss 302
1977 Mercury Cougar Villager, Brougham
1963 Mercury Country Cruiser Base
1964 - 1970 Mercury Cyclone Base
1969 Mercury Cyclone CJ
1970 Mercury Cyclone GT
1969 - 1970 Mercury Cyclone Spoiler
1979 - 1981 Mercury Grand Marquis Colony Park, Base
1979 - 1981 Mercury Marquis Brougham, Base
1963 Mercury Meteor Base, S-33, Custom
1975 - 1980 Mercury Monarch Base
1975 - 1977 Mercury Monarch Ghia
1976 Mercury Monarch Grand Ghia
1968 - 1974 Mercury Montego Base, MX
1972 - 1973 Mercury Montego GT
1970 - 1974 Mercury Montego Villager, MX Brougham
1963 - 1967 Mercury Villager Base
1966 - 1967 Mercury Voyager Base
1978 - 1979 Mercury Zephyr Base, Z7
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4.6 ★★★★★
Based on 6 reviews
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Product Reviews
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WU.
West Palm Beach, US
★★★★★ 4
Good overview of the leading Agentic Framework. Will become outdated quickly.
Format: Paperback
3.5 Stars rounded up. Not a bad place to start if you need to get up to speed fast with Claude Code, understand its vast feature set, how it works under the hood, best practices, and the various agent primitives and how to get the most out of them. Agentic frameworks (Claude Code in particular) are quickly becoming table stakes for anyone working in tech, so it's best to start now. I appreciated the author's ability to flesh out areas where Anthropic's documentation is lacking in depth and nuance, and for some not already working with Claude in their own repos, the fact that he provides "toy" repos where one can experiment with the tools without fear of consequence. Where the book falls short is that most of the stuff in here is already covered pretty well already in Anthropic's docs, or even better so in their free "Skilljar" courses. What's more, some areas are given a bit of a shallow treatment, while others are a bit better done. So it's a bit inconsistent in that sense. Also, I can see how this book will quickly lose its currency in a few months at the pace things are going. Ultimately, for me, the price of this book was a bit rich for my liking given the criticisms above. Still, I feel like I got valuable info that rounded up what I already knew from working with this agentic framework. Recommended.
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Reviewed in the United States on May 28, 2026
B
Brahmananda Reddy
Fort Morgan, US
★★★★★ 5
Practical AI Engineering Beyond Prompts — One of the Better Books on Agentic Coding
Format: Paperback
This book is not another “AI coding hype” book. A lot of books talk about agents at a very high level. This one actually explains how things work when you try to use them inside real development workflows. That was the biggest difference for me. What I liked most was the focus on context engineering, memory, MCP, hooks, subagents, and workflow orchestration instead of just “prompt better.” The author spends time explaining why long-running agent systems fail, how context grows over time, and why most AI coding setups become messy without structure. The examples also feel practical — The HookHub project, Next.js setup, GitHub workflows, Claude memory files, and MCP integrations make it easier to connect theory with actual implementation. From my retail domain experience perspective, I could immediately connect this to forecasting and pricing workflows. For example: * agents helping analysts generate specs before model development * automated code review for promo forecasting pipelines * isolated subagents for pricing, promotions, assortment * persistent memory for business rules across teams * MCP integrations to pull context from internal systems safely The section around context isolation and subagents especially stood out because that is very similar to how enterprise forecasting teams already operate in reality. Different teams own different decision spaces. One thing I appreciated: the author does not oversell AI. There is a strong focus on constraints, context pollution, hallucinations, performance degradation, and workflow reliability. That makes the book feel grounded instead of marketing-heavy. This is not for complete beginners though. If someone has never worked with Git, APIs, coding agents, or LLM workflows, parts of the book may feel overwhelming early on. The author clearly says this is not beginner-level content. Overall, probably one of the more practical books I have read recently on agentic coding systems. Good for: * software engineers * AI engineers * enterprise architecture teams * technical product teams * analytics leaders trying to operationalize AI development workflows Especially useful if your organization is trying to move from “AI demos” into actual production workflows.
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Reviewed in the United States on May 20, 2026
U
UA
Massapequa, US
★★★★★ 5
A Good Reality Check on How AI Agents Actually Work in Enterprise Systems
Format: Paperback
Most AI books stop at prompts. This one goes deeper into how agent systems actually behave once you try to use them inside large workflows with memory, tools, permissions, automation, and multiple agents working together. That part felt very relevant for healthcare and enterprise environments. The book does a good job explaining why context engineering matters and how poor context handling creates hallucinations, inconsistent outputs, and degraded performance over time. Honestly, that is one of the biggest problems organizations underestimate right now. In healthcare workflows, context matters a lot: * prior interactions * business rules * auditability * escalation logic * safety constraints * tool permissions * workflow boundaries The sections on persistent memory, scoped context, subagents, and structured workflows connected strongly to that reality. I work in enterprise analytics, and while reading this book I kept thinking about use cases like: * pharmacy workflow automation * prior authorization support systems * coding assistants for healthcare engineering teams * AI copilots for operational analytics * agent-based escalation systems * claims and workflow orchestration The MCP chapters were also useful because they explain integration challenges clearly instead of treating tooling as magic. What made this book stand out for me was the balance between implementation and architecture. The author explains: * why long contexts fail * how context poisoning happens * why isolation matters * when parallel agents help * when they actually create more complexity That level of honesty is missing in many AI books right now. Another thing: the examples are not overly academic — The Next.js project setup, GitHub automation, Claude desktop workflows, memory systems, hooks, and subagents make the learning process feel practical and hands-on. One limitation: this book assumes technical background. Someone completely new to coding agents, LLMs, Git, or development workflows may struggle in the first few chapters. But for engineers, AI teams, enterprise architects, and technical leaders trying to understand where agentic coding is actually going, this book is worth reading. Especially for organizations trying to operationalize AI safely instead of just experimenting with chatbots.
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Reviewed in the United States on May 20, 2026
C
Christopher West
Natrona Heights, US
★★★★★ 5
Great book! Practical and for developers that already use AI!
Format: Paperback
I purchased "Agentic Coding" by Claude Code due to my desire for an alternative to generic "Prompt Template" type resources related to AI-based development. This book accomplishes just that. As opposed to merely viewing Claude Code as a "magic box", the author has explained how to utilize it in conjunction with other actual development processes. The authors' emphasis on "context engineering" (i.e., structuring data/information; managing knowledge in a project; guiding an AI agent to produce consistent results vs. producing random/unknown results) represents the strongest component of the book. It should be noted that the book appears to be intended primarily for experienced developers with prior experience in software development and/or familiarity with AI-based development tools. Should you be familiar with Git, the command-line interface, and/or modern development processes, you may find this resource very helpful. Conversely, I did appreciate the fact that there were no novice-oriented descriptions provided throughout the book. The aspect of the book that I found most valuable, however, is the extremely pragmatic nature of the material contained within. The examples illustrated through developing/maintaining CLAUDE.md files; utilizing Claude Code in combination with GitHub Workflows; employing MCP Servers; and creating multi-agent or sub-agent workflows all seemed to reflect a clear focus on "real world usage" rather than theoretical constructs. In addition, each chapter builds upon previous chapters in such a manner as to provide a logical progression through which the reader can easily understand and ultimately implement the concepts learned. I also appreciated that the author included guidance on responsible utilization of the tool(s), as well as maintaining control over what changes are made by the agent. While numerous books regarding AI focus solely on what AI tools can accomplish, this book addresses both how to utilize these tools effectively in a real codebase, as well as responsibility and safety considerations. In summary, this is not a book for individuals completely inexperienced in either programming or generative AI. However, if you are currently experimenting with tools such as Claude, Cursor, GitHub Actions, or MCP, this is likely one of the more useful and practical books available on the subject. Recommended for software engineers seeking to transition from simply "prompting an AI" into establishing a repeatable/professional workflow process surrounding agentic coding.
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Reviewed in the United States on April 11, 2026
P
Paul Pollock
Carnegie, US
★★★★★ 4
⭐⭐⭐⭐ (so far)
Format: Paperback
I'm maybe a third of the way through this and already rethinking how I talk to coding agents. The reframe from "prompt engineering" to "context engineering" sounds like semantics until Marco walks you through why context poisoning, context clash, the Goldilocks zone for system prompts. That chapter alone reorganized something in my head. I keep going back to the line about garbage in, garbage out being the real reason agentic systems underperform. The hands-on stuff lands well too. Building the HookHub project from scratch, wiring up Playwright MCP, watching Claude generate a CLAUDE.md file and then not automatically loading a memory file you just created — that moment where you expect magic and get silence instead? That's the kind of honest teaching I appreciate. It made the "why" behind memory hierarchies click.
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Reviewed in the United States on May 12, 2026

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