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.
npx skills add jtsang4/efficient-coding --skill agent-native-redesigngit clone --depth 1 https://github.com/jtsang4/efficient-codingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jtsang4/efficient-coding/agent-native-redesign)<a href="https://agentmods.dev/skills/jtsang4/efficient-coding/agent-native-redesign"><img src="https://agentmods.dev/badge/skills/jtsang4/efficient-coding/agent-native-redesign/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jtsang4/efficient-coding/agent-native-redesign"><img src="https://agentmods.dev/badge/skills/jtsang4/efficient-coding/agent-native-redesign.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00079 | $0.01357 |
| Opus 5.5 | $0.00032 | $0.00543 |
| Sonnet 5.5 | $0.00016 | $0.00271 |
| Haiku 4.5 | $0.00008 | $0.00136 |
Grade A, and why
agent-native-redesign 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent-native redesign
Explore how the subject should work if intelligent execution were an abundant resource. Question structures inherited from scarce human attention, expensive coordination, earlier model capabilities, and historical implementation choices. Design around the desired outcome, with agents able to perform sustained work and use feedback to improve results.
Establish the subject and outcome
Infer the subject from the active conversation and relevant available artifacts. Use the current project as the subject when the conversation does not identify a different one. Honor the user's additional focus and constraints. Inspect enough context to ground the analysis; do not default to analyzing a repository when the conversation concerns a different process or scenario.
Briefly state what is being redesigned, who benefits, and what successful results look like. Proceed on a reasonable stated interpretation; ask a focused question only when the subject or a consequential ambiguity cannot be resolved from context.
Understand the current path from input to outcome: decisions, execution, handoffs, information, and feedback. Ground claims about the current system in observed evidence, and label inferred explanations for why it was designed that way.
Separate inherited assumptions from necessary conditions
Find the assumptions that materially shape the current approach. For each important assumption, connect its original rationale to the structure it created, then examine whether that rationale still applies under abundant agent capacity.
Useful lenses include:
- Human attention and coordination: queues, batch schedules, role boundaries, manual handoffs, sampling, and work left undone because individualized handling was too expensive.
- Earlier capability limits: tiny task boundaries, rigid categories, shallow context, or mandatory human interpretation that may reflect outdated beliefs about models and agents.
- Historical implementation: interfaces, architecture, organizational ownership, or compatibility choices treated as requirements without examining the outcome they serve.
- Access to information and action: knowledge trapped in people's heads, chat threads, or interfaces; missing machine-accessible state and feedback that prevent agents from completing work independently.
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.
- 1mo ago First seen · 72 lines · 79 tokens per session scan A 47031cc795d7
agent-native-redesign is a skill published in the GitHub repository jtsang4/efficient-coding (2 stars, last pushed 8d ago), licensed MIT. It adds 79 tokens to every session and 1,357 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-13.
Other skills, from other repositories
plugin-creator
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cw-gates
Use before claiming any Codewhale change is done, green, or ready to land: the focused-to-broad verification ladder, the budget checks CI enforces, and the rules for what counts as a passing test.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.