Borrowing it
Nothing to install: this file belongs to dbareagimeno/enu. 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/dbareagimeno/enu/develop/.claude/skills/hallazgo/SKILL.mdgit clone --depth 1 https://github.com/dbareagimeno/enuWrote 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/dbareagimeno/enu/hallazgo)<a href="https://agentmods.dev/skills/dbareagimeno/enu/hallazgo"><img src="https://agentmods.dev/badge/skills/dbareagimeno/enu/hallazgo/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dbareagimeno/enu/hallazgo"><img src="https://agentmods.dev/badge/skills/dbareagimeno/enu/hallazgo.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00093 | $0.01194 |
| Opus 5.5 | $0.00037 | $0.00478 |
| Sonnet 5.5 | $0.00019 | $0.00239 |
| Haiku 4.5 | $0.00009 | $0.00119 |
Grade A, and why
hallazgo 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 2mo 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ciclo de un hallazgo G##
Una grieta que la v1 necesita cerrada se resuelve una a una y no está hecha hasta ser coherente en todos los documentos. El código es el último eslabón de la cadena, no el primero: si el hallazgo nació implementando, la implementación espera a que los documentos cierren.
Pasos
-
Registrar. Crea
docs/findings/gNN-<slug>.mdcon el siguiente número G## libre (el índice dedocs/findings/README.mdlo dice; ojo a los huecos reservados que anota). El fichero lleva frontmatter de tipo hallazgo (type: hallazgo,id: GNN,status: abierto,origin: <de dónde salió>) y el formato de siempre: H1# GNN · <título corto> — <docs afectados>, y los bloques Problema (la grieta, con la frase textual de la espec que se queda corta), Impacto (a quién afecta y con qué urgencia) y Opciones ((a), (b), (c)... cada una con su porqué). Añade su fila a la tabla índice dedocs/findings/README.mdy actualiza el contador de la cabecera (N registradas, N resueltas, N abiertas). -
¿Ya es expresable? Antes de inventar API, lanza el agente
verificadorcon el hallazgo: su mandato es demostrar que el escenario ya se compone con la API existente (el precedente: el semáforo connu.task.future). Si vuelve FALSO POSITIVO con la composición como evidencia, el G## se cierra como demostrado-expresable: se documenta la composición en la resolución y no se toca la API. Este paso mata la mayoría de hallazgos baratos. -
Decidir. Presenta las opciones al usuario con tu recomendación motivada (qué rompe menos, qué compone mejor, qué precedente sienta). Si la resolución añade o toca
api.md, lanza antesjuez-filosofiacon la propuesta: una OBJECIÓN bloqueante vuelve al usuario. Toda adición a la API exige bump denu.version.apiy crece solo por adición. -
Aplicar a TODOS los documentos. Busca con Grep cada contrato y pseudocódigo que presuponía la semántica vieja (
agente.md,providers.md,sesiones.md,chat.md,guia-plugins.md,pseudocodigo.md,arquitectura.md,modelo-ejecucion.md... el corpus completo) y actualízalos. Deja el rastro: cada cambio cita el G## que lo motiva.
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.
- 2mo ago First seen · 72 lines · 93 tokens per session scan A e1e9e40a8c06
hallazgo is a skill published in the GitHub repository dbareagimeno/enu (2 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,194 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
optimization
Use when improving performance, latency, throughput, memory usage, or general efficiency. Start by defining target metrics, measuring comprehensively, attributing bottlenecks, validating with static analysis, and prioritizing macro-optimizations before micro-optimizations.
pi-observability-local-dev
Use when developing or reviewing packages/pi-observability dashboard/server changes locally, especially rebuilding dist, restarting the 43190 server, validating UI behavior, or keeping the local dashboard in sync with source edits.
debug
Systematic debugging that finds the root cause before changing any code — collect symptoms, trace the code path, check what changed, reproduce deterministically, form a testable hypothesis, then fix the cause (not the symptom) and prove it with a regression test. Use when the user reports a bug, a crash, a failing…
investigate
A structured root-cause investigation method for software bugs. It moves from history and reproduction through tested hypotheses, a focused fix, verification, and a report.
bugfix-triage
A method for locating the cause of a bug before changing code, using a minimal reproduction and step-by-step narrowing of the failing input and code path.
loop-engineering-bruteforce
Use the vendored loop-engineering toolkit to scaffold, audit, and continuously improve autonomous loops in this GodCoder repository, and pair the result with the ResearchSwarm bridge for route/log/optimize memory.