Borrowing it
Nothing to install: this file belongs to senda-labs/DQIII8. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/senda-labs/DQIII8/main/.claude/commands/evolve.mdgit clone --depth 1 https://github.com/senda-labs/DQIII8Wrote 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/commands/senda-labs/dqiii8/evolve)<a href="https://agentmods.dev/commands/senda-labs/dqiii8/evolve"><img src="https://agentmods.dev/badge/commands/senda-labs/dqiii8/evolve.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 5.1 | $0.00041 | $0.00549 |
| Opus 5 | $0.00020 | $0.00275 |
| Sonnet 5 | $0.00008 | $0.00110 |
| Haiku 4.5 | $0.00004 | $0.00055 |
Grade A, and why
evolve scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
DESHABILITADO:
bin/evolve.pyno existe. Este comando fallará si se invoca. Documentado tal cual (no implementado), no eliminado — ver TODO abajo.
/evolve — Convertir Instincts en Skills
Lee instincts con alta confianza o alta frecuencia de aplicacion de dqiii8.db,
los agrupa por keyword raiz, y genera skill drafts para clusters con 3+ instincts.
Uso
/evolve
/evolve --min-confidence 0.5
/evolve --min-applied 10
/evolve --min-cluster 2
/evolve --dry-run
Que hace
- Lee instincts con
confidence >= 0.7 OR times_applied >= 5(ajustable) - Agrupa por primer segmento del keyword (
ssim-hacking→ raizssim) - Para clusters con 3+ instincts: escribe
skills-registry/custom/evolved/[raiz].md - Registra en
skills-registry/INDEX.mdcon statusPENDIENTE_REVISION
Implementacion
python3 /root/dqiii8/bin/evolve.py "$@"
Flujo de aprobacion
- Revisar el draft en
skills-registry/custom/evolved/[raiz].md - Editar las secciones "Reglas consolidadas" y "Anti-patrones"
- Cambiar status en
INDEX.mda✅ APROBADA - Mover de
custom/evolved/acustom/[nombre]/SKILL.md
Notas DQIII8
- Threshold por defecto:
confidence >= 0.7 OR times_applied >= 5 - Las skills en
custom/evolved/NO se cargan en sesion — requieren aprobacion manual - El comando es idempotente: re-ejecutar no sobreescribe skills existentes en INDEX
- Las skills evolucionadas son mas precisas que las importadas de ECC/ruflo porque provienen de comportamiento real del sistema (5.687+ acciones acumuladas)
- Complementa P3c (Intelligence Loop): P3c ajusta confidence, /evolve la convierte en skill
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.
- 6d ago First seen · 55 lines · 41 tokens per session scan A 2e83b1ef0596
evolve is a command published in the GitHub repository senda-labs/DQIII8 (11 stars, last pushed 17d ago), licensed MIT. It adds 41 tokens to every session and 549 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 commands, from other repositories
evolve
Trigger harness evolution: analyze evaluation logs, propose changes, apply to experimental pool.
run
Run a task through the adaptive-harness pipeline explicitly.
status
Show adaptive-harness pool state, performance stats, and evolution history.
eval
Manually evaluate the last task result or a specific change.
prd-score
Score an existing PRD against the 100-point AI-optimization framework.
pr-enhance
Command "pr-enhance" from shyftlabs/continuum, covering pr-enhance, usage, options, examples and enhance pr.