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qualit527
GitHub-Creator-Profil

qualit527

Repository-Ansicht von 7 gesammelten Skills in 1 GitHub-Repositories.

gesammelte Skills
7
Repositories
1
aktualisiert
25. Apr. 2026
Repository-Explorer

Repositories und repräsentative Skills

demo-presenter
Technische RedakteureProduzenten und Regisseure

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. Apr. 2026
review-framework
Softwareentwickler

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. Apr. 2026
autoqec-run
Datenwissenschaftler

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. Apr. 2026
read-zulip
Projektmanagementspezialisten

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. Apr. 2026
diagnose-failure
Softwareentwickler

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. Apr. 2026
review-log
Hochschullehrer für Soziologie

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. Apr. 2026
verify-decoder
Datenwissenschaftler

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. Apr. 2026
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