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qualit527
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qualit527

Vista por repositorio de 7 skills recopiladas en 1 repositorios de GitHub.

skills recopiladas
7
repositorios
1
actualizado
25 abr 2026
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Repositorios y skills representativas

demo-presenter
Redactores técnicosProductores y directores

Use when preparing an AutoQEC demo walkthrough, advisor presentation, hackathon pitch, recorded narration, or evidence-backed explanation of why the demos matter and why their outputs are correct.

25 abr 2026
review-framework
Desarrolladores de software

Read the artifacts of a completed AutoQEC run and propose improvements to the framework code itself — DSL gaps, weak baselines, miscalibrated safety thresholds, prompt drift, env limitations, orchestration friction. Advisory only; never edits framework files.…

25 abr 2026
autoqec-run
Científicos de datos

Run the AutoQEC research loop on a given env YAML. Orchestrates the autoqec-ideator / autoqec-coder / autoqec-analyst subagents via the Agent tool, invokes the Runner CLI for training + evaluation, and writes history.jsonl + pareto.json. Use when the user…

24 abr 2026
read-zulip
Especialistas en gestión de proyectos

Read Zulip stream/topic history for project context, summarize decisions, and recover requirements or action items. Use when task context may depend on prior Zulip discussion rather than only repository files.

23 abr 2026
diagnose-failure
Desarrolladores de software

Inspect a stalled or failed run, identify root cause (bad hyperparameter / NaN pattern / OOM / env misconfig), and recommend a fix. Does NOT apply fixes autonomously.

22 abr 2026
review-log
Profesores postsecundarios de sociología

Read an entire runs/<id>/log.md and assess research narrative coherence, identify stuck hypotheses, detect overfitting signs, and write a review markdown. Use after a full run completes or when a loop has been stuck for many rounds.

22 abr 2026
verify-decoder
Científicos de datos

Audit a predecoder checkpoint against holdout seeds. Runs independent_eval (3 fair-baseline guards) and interprets borderline cases with LLM reasoning. Use when a round produces a promising Δ_LER and the user wants to confirm it is not a reward-hacking…

22 abr 2026
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