SKU: 12632955200

ACCEL - Fuel Injector - 15 lb/hr - EV1 Minitimer - High Impedance - 150115

Sale price$89.95 Regular price$99.95
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Description

ACCEL - Fuel Injector - 15 lb/hr - EV1 Minitimer - High Impedance - 150115Overview: ACCEL Performance fuel injectors provides precise control of fuel delivery & atomization for improved throttle response. They are all new units; not modified stock units or remanufactured from used cores. They feature a low mass disc design that ensures accurate metering control even at ultra low & high duty cycles and higher fuel pressures. ACCEL performance Injectors can be used with gasoline or E85 and are great for blown applications

Overview:

ACCEL Performance fuel injectors provides precise control of fuel delivery & atomization for improved throttle response. They are all new units; not modified stock units or remanufactured from used cores. They feature a low-mass disc design that ensures accurate metering control even at ultra-low & high duty cycles and higher fuel pressures. ACCEL performance Injectors can be used with gasoline or E85 and are great for blown applications where increased fuel pressure and precise metering are required. State-of-the-art manufacturing processes reduce flow deviations between injectors, which improve performance. Low- and High-impedance designs are available to match most commercial applications.

Features:

  • Provides precise control of fuel delivery & atomization for improved throttle response
  • All new units; not modified stock units or remanufactured from used cores
  • New low-mass disc design ensures accurate metering control even at ultra-low & high duty cycles
  • Operates at higher fuel pressures without loss of metering control
  • Great for blown applications where increased fuel pressure and precise metering are required
  • State-of-the-art manufacturing processes reduce flow deviations between injectors to improve performance

Application:

Year Make Model Submodel Engine Size
1990 Ford F-150 302/5 V8
1987 - 1996 Ford E-350 Econoline 300/4.9 L6
1987 - 1996 Ford E-350 Econoline Club Wagon 300/4.9 L6
1987 - 1996 Ford F-150 300/4.9 L6
1995 Ford F-150 Special 300/4.9 L6
1995 Ford F-150 Eddie Bauer 300/4.9 L6
1993 - 1995 Ford F-150 XL 300/4.9 L6
1993 - 1995 Ford F-150 XLT 300/4.9 L6
1987 - 1996 Ford F-250 300/4.9 L6
1987 - 1996 Ford F-350 300/4.9 L6
1991 Ford F-350 Custom 300/4.9 L6
1991 Ford F-350 XL 300/4.9 L6
1991 Ford F-350 XLT Lariat 300/4.9 L6
1987 - 1996 Ford E-250 Econoline 300/4.9 L6
1987 - 1991 Ford E-250 Econoline Club Wagon 300/4.9 L6
1987 - 1996 Ford E-150 Econoline 300/4.9 L6
1987 - 1996 Ford E-150 Econoline Club Wagon 300/4.9 L6
1994 - 1998 Ford Mustang Base 232/3.8 V6
1989 - 1995 Ford Taurus GL 232/3.8 V6
1989 - 1995 Ford Taurus LX 232/3.8 V6
1991 Ford Taurus L 232/3.8 V6
1995 Ford Taurus SE 232/3.8 V6
1991 - 1992 Ford Thunderbird Base 232/3.8 V6
1991 - 1993 Ford Thunderbird LX 232/3.8 V6
1991 - 1993 Ford Thunderbird Super Coupe 232/3.8 V6
1994 - 1997 Ford Thunderbird 232/3.8 V6
1991 Mercury Cougar LS 232/3.8 V6
1989 - 1994 Mercury Sable 232/3.8 V6
1995 Mercury Sable GS 232/3.8 V6
1995 Mercury Sable LS 232/3.8 V6
1995 - 1999 Chevrolet Camaro Base 231/3.8 V6
1996 - 1997 Chevrolet Camaro RS 231/3.8 V6
1995 - 1999 Pontiac Firebird Base 231/3.8 V6
1993 - 1995 Chevrolet Camaro Base 207/3.4 V6
1993 - 1995 Pontiac Firebird Base 207/3.4 V6
1989 - 1993 Buick Century 204/3.3 V6
1990 Buick Skylark Custom 204/3.3 V6
1990 Buick Skylark Gran Sport 204/3.3 V6
1990 Buick Skylark Luxury Edition 204/3.3 V6
1989 - 1993 Buick Skylark 204/3.3 V6
1992 - 1993 Pontiac Grand Am 204/3.3 V6
1992 - 1993 Oldsmobile Achieva S 204/3.3 V6
1992 Oldsmobile Achieva SC 204/3.3 V6
1992 - 1993 Oldsmobile Achieva SL 204/3.3 V6
1989 Oldsmobile Cutlass Calais S 204/3.3 V6
1989 - 1991 Oldsmobile Cutlass Calais SL 204/3.3 V6
1989 - 1990 Oldsmobile Cutlass Ciera Base 204/3.3 V6
1989 - 1990 Oldsmobile Cutlass Ciera GT 204/3.3 V6
1989 - 1990 Oldsmobile Cutlass Ciera SL 204/3.3 V6
1990 Oldsmobile Cutlass Ciera S 204/3.3 V6
1991 - 1993 Oldsmobile Cutlass Ciera 204/3.3 V6
1989 - 1992 Oldsmobile Cutlass Cruiser 204/3.3 V6
1994 - 1996 Buick Century 189/3.1 V6
1994 - 1996 Buick Regal Custom 189/3.1 V6
1994 - 1998 Buick Skylark 189/3.1 V6
1990 Chevrolet Beretta Base 189/3.1 V6
1990 Chevrolet Beretta GT 189/3.1 V6
1990 Chevrolet Beretta Indianapolis 500 Pace Car 189/3.1 V6
1991 - 1996 Chevrolet Beretta 189/3.1 V6
1990 - 1992 Chevrolet Camaro RS 189/3.1 V6
1992 Chevrolet Camaro RS Heritage Edition 189/3.1 V6
1991 - 1994 Chevrolet Cavalier RS 189/3.1 V6
1990 - 1992 Pontiac Firebird Base 189/3.1 V6
1994 - 1998 Pontiac Grand Am 189/3.1 V6
1996 Pontiac Grand Prix Base 189/3.1 V6
1992 - 1999 Pontiac Grand Prix SE 189/3.1 V6
1990 - 1994 Pontiac Grand Prix 189/3.1 V6
1992 - 1993 Pontiac Grand Prix GT 189/3.1 V6
1992 - 1993 Pontiac Grand Prix LE 189/3.1 V6
1992 - 1993 Pontiac Grand Prix STE 189/3.1 V6
1991 - 1992 Pontiac Sunbird GT 189/3.1 V6
1991 - 1992 Pontiac Sunbird SE 189/3.1 V6
1991 Pontiac Sunbird LE 189/3.1 V6
1993 - 1994 Pontiac Sunbird 189/3.1 V6
1991 - 1994 Chevrolet Cavalier Z24 189/3.1 V6
1990 Chevrolet Celebrity 189/3.1 V6
1990 - 1996 Chevrolet Corsica 189/3.1 V6
1990 - 1999 Chevrolet Lumina 189/3.1 V6
1991 - 1994 Chevrolet Lumina Base 189/3.1 V6
1991 - 1994 Chevrolet Lumina Euro 189/3.1 V6
1997 - 1999 Chevrolet Malibu 189/3.1 V6
1995 - 1999 Chevrolet Monte Carlo LS 189/3.1 V6
1994 - 1998 Oldsmobile Achieva 189/3.1 V6
1994 - 1996 Oldsmobile Cutlass Ciera 189/3.1 V6
1990 - 1996 Oldsmobile Cutlass Supreme 189/3.1 V6
1988 - 1991 Pontiac 6000 189/3.1 V6
1989 Pontiac 6000 Touring 189/3.1 V6
1989 Pontiac 6000 STE 189/3.1 V6
1991 - 1997 Ford Ranger 182/3 V6
1986 - 1997 Ford Aerostar 182/3 V6
1992 - 1994 Ford Aerostar Base 182/3 V6
1992 - 1994 Ford Aerostar XL 182/3 V6
1992 - 1994 Ford Aerostar XL Plus 182/3 V6
1992 - 1994 Ford Aerostar XL Sport 182/3 V6
1992 - 1994 Ford Aerostar XLT 182/3 V6
1992 - 1994 Ford Aerostar XLT Plus 182/3 V6
1992 - 1994 Ford Aerostar XLT Sport 182/3 V6
1996 - 1998 Ford Windstar GL 182/3 V6
1996 - 1998 Ford Windstar Base 182/3 V6
1998 Ford Windstar 3.0L 182/3 V6
1986 - 1992 Ford Ranger 177/2.9 V6
1987 - 1989 Buick Century 173/2.8 V6
1985 Buick Skylark 173/2.8 V6
1985 - 1986 Cadillac Cimarron 173/2.8 V6
1988 Chevrolet Camaro Base 173/2.8 V6
1985 - 1987 Chevrolet Camaro Sport 173/2.8 V6
1985 - 1986 Chevrolet Camaro Berlinetta 173/2.8 V6
1989 Chevrolet Camaro RS 173/2.8 V6
1987 Chevrolet Camaro LT 173/2.8 V6
1985 Chevrolet Cavalier 173/2.8 V6
1986 Chevrolet Cavalier RS 173/2.8 V6
1986 Chevrolet Cavalier Z24 173/2.8 V6
1985 - 1986 Pontiac Fiero GT 173/2.8 V6
1985 - 1986 Pontiac Fiero SE 173/2.8 V6
1985 Pontiac Fiero Sport 173/2.8 V6
1985 - 1989 Pontiac Firebird Base 173/2.8 V6
1985 - 1986 Pontiac Firebird S/E 173/2.8 V6
1985 - 1989 Chevrolet Celebrity 173/2.8 V6
1985 Chevrolet Citation II 173/2.8 V6
1988 Oldsmobile Cutlass Ciera S 173/2.8 V6
1988 - 1989 Oldsmobile Cutlass Ciera Base 173/2.8 V6
1988 Oldsmobile Cutlass Ciera LS 173/2.8 V6
1988 - 1989 Oldsmobile Cutlass Ciera SL 173/2.8 V6
1988 Oldsmobile Cutlass Ciera Brougham 173/2.8 V6
1986 - 1987 Oldsmobile Cutlass Ciera 173/2.8 V6
1987 - 1989 Oldsmobile Cutlass Cruiser 173/2.8 V6
1988 Oldsmobile Cutlass Supreme Base 173/2.8 V6
1988 Oldsmobile Cutlass Supreme SL 173/2.8 V6
1988 Oldsmobile Cutlass Supreme International 173/2.8 V6
1988 Oldsmobile Cutlass Supreme Indy 500 Pace Car 173/2.8 V6
1985 Oldsmobile Firenza 173/2.8 V6
1986 - 1987 Oldsmobile Firenza GT 173/2.8 V6
1986 - 1988 Pontiac 6000 173/2.8 V6
1989 Pontiac 6000 LE 173/2.8 V6
1989 Pontiac 6000 Special Edition 173/2.8 V6
1985 Ford Bronco II XLS 134/2.3 L4
1997 - 1998 Buick Century
1988 - 1993 Buick Regal
1988 - 1989 Pontiac Grand Prix
1988 - 1989 Ford Aerostar
1986 - 1990 Ford Bronco II
1997 - 1998 Oldsmobile Cutlass
1993 - 1994 Oldsmobile Cutlass Cruiser
1989 - 1997 Oldsmobile Cutlass Supreme
1995 Ford Windstar
1994 - 1996 Mazda B3000

Specs:

Body Diameter 0.97
Body Length 2.85"
Body Style EV1
Brand ACCEL
Color Silver
Connector Series EV1 - Jetronic / Minitimer
Emission Code 2
Flow Rated @ 43.5 PSI
Impedance High 14.5 Ohm
O-Ring to O-Ring 2.39"
Product Type MPFI Fuel Injectors
Qty 1
Rated Injector Flow 15 lb/hr
Warning California Proposition 65
Weight 0.22
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SKU: 12632955200

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4.3 ★★★★★
Based on 158 reviews
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Product Reviews
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Verified Purchase
WU.
Lowell, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 28, 2026
B
Brahmananda Reddy
Whiting, 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
Phoenix, 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
Lowell, 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
Port Orchard, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 12, 2026

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