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/drn/dots/contestnpx skills add drn/dots --skill contestgit clone --depth 1 https://github.com/drn/dotsWhat 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 | $0.00026 | $0.02520 |
| Opus 5 | $0.00013 | $0.01260 |
| Sonnet 5 | $0.00005 | $0.00504 |
| Haiku 4.5 | $0.00003 | $0.00252 |
Grade A, and why
contest 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.
How it starts
The opening of the file, as written. The whole thing — 329 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competing Implementations
Spawn multiple implementers to build different solutions to the same problem. A judge evaluates and picks the best one.
Prerequisites
Agent teams must be enabled in Claude Code settings:
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1
If agent teams are not enabled, report: "Agent teams required. Add CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 to your Claude Code settings (env section)." and stop.
Arguments
$ARGUMENTS- Required: the problem to solve, ideally with evaluation criteria (e.g., "optimize the search function -- prioritize readability over raw speed")
If no arguments are provided, ask the user what they want implemented.
Context
- Current branch: !
git branch --show-current - Git status: !
git status --short - Base ref: !
git branch -r 2>/dev/null | grep -oE 'origin/(main|master)' | head -1 - Project root: !
pwd - Project type: !
find . -maxdepth 1 \( -name go.mod -o -name Gemfile -o -name package.json -o -name Cargo.toml -o -name pyproject.toml -o -name setup.py -o -name requirements.txt -o -name pom.xml -o -name build.gradle -o -name Makefile \) 2>/dev/null | head -5 - Recent commits: !
git log --oneline -5 - Test files: !
find . -maxdepth 4 -name "*_test.*" -o -name "*.test.*" -o -name "*_spec.*" 2>/dev/null | head -10
Overview
You are the contest coordinator. Your job is to frame the problem, assign it to multiple implementers working in separate worktrees, and have a judge pick the winner.
Problem: $ARGUMENTS
You do NOT implement or judge yourself. You coordinate the contest.
Why this works: For design problems with multiple valid solutions (performance optimization, API design, architecture choices), building 2-3 options and comparing is faster and more reliable than debating hypothetical trade-offs.
Phase 0: Setup
-
Clean working tree: If there are uncommitted changes, commit them with message
"WIP: pre-contest state"before proceeding. -
Analyze the problem and define evaluation criteria. If the user didn't specify criteria, propose them:
- Correctness (tests pass)
- Readability / maintainability
- Performance (if relevant)
- Simplicity (fewer lines, fewer dependencies)
- Consistency with existing codebase
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 · 329 lines · 26 tokens per session scan A ab60e257c2f7
contest is a skill published in the GitHub repository drn/dots (23 stars, last pushed 4d ago), licensed MIT. It adds 26 tokens to every session and 2,520 once invoked, about $0.0001 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.
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chat-pet-sprite-creation
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cpu-profile-analysis
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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.