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 commands/liortesta/clawdagent/competegit clone --depth 1 https://github.com/liortesta/ClawdAgentWhat 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 | $0.00000 | $0.00417 |
| Opus 5 | $0.00000 | $0.00209 |
| Sonnet 5 | $0.00000 | $0.00083 |
| Haiku 4.5 | $0.00000 | $0.00042 |
Grade A, and why
compete 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 3d 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
Competing Solutions
For the given task, create 3 DIFFERENT implementations and pick the best one.
Process:
- Understand the task requirements
- Design 3 different approaches:
- Approach A: Optimize for simplicity and readability
- Approach B: Optimize for performance and efficiency
- Approach C: Optimize for extensibility and maintainability
- Implement each approach using separate subagents in parallel
- Test each implementation — all must pass the same test suite
- Benchmark (if applicable) — compare performance metrics
- Evaluate using the CTO agent criteria:
- Correctness (does it work?)
- Code quality (is it clean?)
- Performance (is it fast?)
- Maintainability (can we change it later?)
- Simplicity (is it easy to understand?)
- Pick the winner — merge winning approach, discard others
- Document why the winner was chosen in CLAUDE.md Architecture Decisions
Output Format:
## Competition Results
| Criteria | Approach A | Approach B | Approach C |
|----------|-----------|-----------|-----------|
| Correctness | PASS/FAIL | PASS/FAIL | PASS/FAIL |
| Lines of Code | X | Y | Z |
| Performance | Xms | Yms | Zms |
| Readability | 1-10 | 1-10 | 1-10 |
| Maintainability | 1-10 | 1-10 | 1-10 |
**WINNER**: Approach [X]
**REASON**: [why this one]
**ADR**: Added to CLAUDE.md
Rules:
- All 3 approaches must pass the SAME tests
- The winner must score highest OVERALL, not just in one category
- If all 3 fail tests, go back to planning — don't pick "least broken"
Task: $ARGUMENTS
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.
- 3d ago First seen · 50 lines · 0 tokens per session scan A 7dd9ec6c4f73
compete is a command published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 6d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 417 tokens. 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 commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.