concordance-co
Official@concordance-co
Asymmetrical experiments
Agent Skills by concordance-co
Showing 13 vetted skills indexed across 2 GitHub repositories.
concordance-coconstructing-workflows
Design and edit pipelines_v2 workflows with runner specs and metadata.
concordance-cobenchmark-validation
Assess benchmark accessibility, runnability, and label sufficiency for interpretability analysis.
concordance-cosynthetic-data-generation
Design and validate synthetic data benchmarks for mechanistic interpretability experiments.
concordance-copipelines-v2-run-ops
Plan, run, and monitor existing pipelines_v2 workflows using the pipelines_v2 CLI.
concordance-colatent-label-data-augmentation
Augment latent labels to repair benchmarks and reduce confounds.
concordance-cobenchmark-mech-interp-analysis
Plan and review mechanistic analysis workflows for validated benchmarks.
concordance-coconstructing-llm-probes
Extract LLM hidden-state activations and train linear or nonlinear probes.
concordance-cobenchmark-to-latent-labels
Convert benchmark native labels into a latent-label specification for mechanistic interpretability.
concordance-cogarden-docs
Audit repository Markdown files against current code and produce citation-backed punch lists.
concordance-cofrontend-design
Generate production-grade frontend interfaces with typography, color, and accessibility systems.
concordance-coactivation-patching-causal-evals
Plan and execute activation patching experiments with donor-target interchange and same-label controls.
concordance-comechanistic-interventions
Define intervention plans with target behavior, success criteria, and paired controls.
concordance-coconcordance-mod
Build and manage Concordance mods for LLM generation interventions.
Frequently Asked Questions About concordance-co
FAQPage SchemaWhat specific research tasks are enabled by these capabilities?▼
These capabilities enable rigorous mechanistic interpretability research, including the construction of hidden-state probes, the execution of causal activation patching experiments, and the systematic validation of synthetic data benchmarks to reduce model confounds.
Which technical personas benefit from these interpretability methods?▼
These methods are designed for research engineers and interpretability scientists focused on neural network transparency, causal evaluation, and the systematic auditing of internal model representations through controlled intervention experiments.
What are the primary prerequisites for running these interpretability experiments?▼
Users require access to model hidden-state activations, a defined benchmark dataset, and a configured environment capable of executing pipelines_v2 specifications for managing intervention plans and causal evaluation runs.