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/nexus-substrate/nexus-agents/implement-featurenpx skills add nexus-substrate/nexus-agents --skill implement-featuregit clone --depth 1 https://github.com/nexus-substrate/nexus-agentsWrote 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/nexus-substrate/nexus-agents/implement-feature)<a href="https://agentmods.dev/skills/nexus-substrate/nexus-agents/implement-feature"><img src="https://agentmods.dev/badge/skills/nexus-substrate/nexus-agents/implement-feature.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.01534 |
| Opus 5 | $0.00019 | $0.00767 |
| Sonnet 5 | $0.00008 | $0.00307 |
| Haiku 4.5 | $0.00004 | $0.00153 |
Grade A, and why
implement-feature 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 4d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Feature Skill
Full workflow: CONTRIBUTION_GUIDE.md
Pre-Implementation Checklist
- Verify context:
TZ='America/New_York' date && git status - Check/create GitHub issue:
gh issue list --state open - Check research registry if implementing a technique
Hard Design Decisions — Constraint-Divergent Design
When the problem has multiple plausible approaches (e.g., "event-driven or polling?", "synchronous vs queue-based?", "monolithic helper vs split modules?"), do not generate the same solution three times with different variable names. Instead, articulate 2–3 distinct constraints first, then sketch one solution per constraint and compare.
Anchor constraints in real tradeoffs:
- "minimize allocations" vs "minimize lines of code" vs "minimize external deps"
- "lowest latency" vs "lowest memory" vs "easiest to test"
- "fewest moving parts" vs "easiest to extend" vs "matches existing pattern X"
Three constraints that each eliminate ~70% of the solution space leave you searching ~2.7% of it — a focused region, not blind sampling. (Pattern adapted from itigges22/ATLAS PlanSearch; the math is theirs.)
Apply this discipline only for genuinely-multivalent decisions. Indicators:
- Multiple plausible architectures where reviewers would reasonably disagree
- A wrong choice would be expensive to reverse (data-shape migration, public API surface, cross-package boundary)
- You catch yourself thinking "I'll just pick one and see what review says" — pick deliberately, by constraint, instead
Out of scope: routine work where the approach is dictated by canonical paths or existing patterns. Don't over-apply.
Implementation Process
Phase 1: Interface First
// Define interface FIRST
interface IFeature {
method(input: Input): Promise<Result<Output, Error>>;
}
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.
- 4d ago First seen · 165 lines · 39 tokens per session scan A 8b80ab43c2c0
implement-feature is a skill published in the GitHub repository nexus-substrate/nexus-agents (18 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 1,534 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…