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06:00

Switching from Superpowers to mattpocock/skills: Less Token Waste, More Control

从Superpowers转向mattpocock/skills：更省token、更可控的Agent工程实践

The author shares a real-world comparison between Superpowers and mattpocock/skills, explaining why they switched. Superpowers uses hooks to enforce a rigid workflow, which is helpful for novices but often overcomplicates simple tasks and burns excessive tokens. mattpocock/skills takes a 'real engineer' approach, giving control back to the user via explicit commands like /grill-with-docs, /to-prd, /to-issues, and /implement. Key advantages: lower token consumption, built-in debugging (/tdd, /diagnosing-bugs), model handoff (/handoff), and architecture refactoring (/improve-codebase-architecture). The author pairs these skills with Fable 5 and Codex 5.5 models, storing PRDs and issues on GitHub for traceability. A candid take for engineers evaluating agent frameworks and tooling.

justinyan.me · 3 min · Agent Engineering · Claude Code Marketplace · Framework · TDD · Token-Optimization
06:00

Superpowers: How to Make an AI Agent Run All Night and Deliver Usable Results

Superpowers：让AI Agent跑通宵且交付可用的秘诀

The author shares their journey from a failed attempt at orchestrating long-running AI agent tasks to discovering the Superpowers Skill Set, which solves the core pain points. Superpowers decomposes the development workflow into three phases: brainstorming, writing-plans, and executing-plans (with subagent-driven-development). Key design elements include: using separate prompt templates (implementer, spec-reviewer, code-quality-reviewer) to enforce separation of concerns; spinning up a fresh subagent for each task to avoid cascading context pollution; using hard constraints like "Never/HARD-GATE" to prevent agent deviation; and enforcing software engineering best practices such as TDD, DRY, and YAGNI. The author argues that with frontier models like Opus 4.8 and Codex GPT-5.5 now being sufficiently capable, the real bottleneck is harness design—using clear specifications and structured processes to make even cheaper models reliable for long-duration tasks.

justinyan.me · 4 min · Agent Architecture · Agent Engineering · Context Engineering · Orchestration · Superpowers
06:00

Superpowers 6: Cutting Build Cost 60% via Autoresearch Loop

Superpowers 6：用自动化研究循环将构建成本降低60%

Superpowers 6 is released, with its biggest improvements driven by an automated research loop. The author used Anthropic's Fable model (briefly available) to systematically optimize their Subagent Driven Development pipeline. Over 36 hours and ~$165 in token spend, 25 experiments were run, yielding a 50% reduction in wall-clock time and 60% reduction in token consumption vs. v5. Key optimizations: merging spec compliance and code review agents, pre-baking review packets to minimize git operations, and dynamic agent allocation based on task type (e.g., using cheap haiku for non-code plans). The post also documents falsified hypotheses (e.g., capping controller thinking backfires) and emphasizes the role of their eval suite in rigorous measurement.

blog.fsck.com · 8 min · Agent Engineering · AI Engineering · Anthropic · Cost Optimization · Evaluation