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 agentmods add skills/emilyli2020/wave/feature-buildernpx skills add emilyLi2020/WAVE --skill feature-buildergit clone --depth 1 https://github.com/emilyLi2020/WAVEWhat 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 | $0.00047 | $0.00445 |
| Opus 5 | $0.00023 | $0.00222 |
| Sonnet 5 | $0.00009 | $0.00089 |
| Haiku 4.5 | $0.00005 | $0.00044 |
Grade A, and why
feature-builder 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 2d 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.
What it actually says
Feature Builder
The user wants to implement a new feature. They have no coding background. Guide them through a structured, safe process.
Step 1: Understand the Feature
Ask the user to describe:
- What the feature should do (in their own words)
- Who will use it
- What it should look like (screenshots, sketches, or descriptions are all fine)
If the description is vague, propose 2-3 concrete interpretations and ask which one is closest.
Step 2: Create the Plan
Generate a plan with exactly these sections:
- Goal: One sentence describing the feature
- Steps: 5-10 numbered steps, each with a single goal
- Files: List every file that will be created or changed
- Dependencies: Any new libraries needed (ask before installing)
- Commands: Copy-paste terminal commands to run
- Manual Test: Step-by-step instructions to verify the feature works
- Rollback Plan: How to undo the changes if something breaks
Step 3: Implement
- Build the smallest vertical slice first: input > minimal processing > visible output
- Implement one step at a time; confirm each step works before moving to the next
- Use the simplest libraries and hosted services available
- Target a visible, working demo in under 60 minutes
Step 4: Verify
After implementation:
- Run the project and capture any errors
- Walk the user through the manual test steps
- If errors occur, explain them in plain English and propose fixes
- Ask: "Does this match what you had in mind?"
Constraints
- Never add features the user did not ask for
- Never make changes outside the scope of this feature
- Keep changes minimal, testable, and reversible
- Prefer high-level libraries over low-level implementations
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.
- 2d ago First seen · 52 lines · 47 tokens per session scan A 8f7d0b45af16
feature-builder is a skill published in the GitHub repository emilyLi2020/WAVE (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 445 once invoked, about $0.0002 per session on Opus 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 2026-08-31.
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