plugins/doc-master/skills/adr-critique/SKILL.md
This skill should be used to audit an existing or legacy ADR, especially one imported, LLM-generated, stale, or never self-critiqued. PROACTIVELY activate on "review this ADR", "critique this ADR", "audit our decision log", "is this ADR any good?", "fix this old ADR", "rewrite this legacy ADR", "ADR cleanup", "ADR quality sweep", "tighten this ADR", "score these ADRs", or "this ADR is too long." Provides: line-by-line ADR critique, filler/hedging/drift detection, and per-line approval workflow.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace adr-critiqueInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Line-by-line auditor for legacy / external / pre-existing ADRs. Surfaces violations of the shared "ADR is not" rules, missing-why reasoning, inconsistency with neighbors, and drift against LikeC4 models. Touches only architect-approved lines.
adr-drafting (legacy corpus)adr-drafting (Phase 6) — they've been through the filter.adr-drafting instead and supersede the original.../_shared/adr-is-not.md (e.g., "Rule 5 — generic best-practice citation").adr-drafting.Load the target ADR. Then glob its neighbors via supersedes, amends, and relates-to links and read each. The audit needs the graph context.
If the ADR cites a LikeC4 file, glob **/*.c4 and read the referenced model.
Walk the ADR top to bottom. For every line that violates a rule, emit one flag using this template:
Original: <verbatim line from ADR>
Violates: <rule number and name from adr-is-not.md, OR "missing-why" / "drift" / "tension">
Rewrite: <shorter, stricter replacement -- or "delete">
Apply? (yes / no / adjust)
Wait for the architect's reply. Only after they answer do you move to the next flag.
If the architect chooses adjust, accept their rewrite (still subject to the shorter-than-original constraint).
Focus on the Decision section. Valid reasoning ties a business concern to an architectural characteristic. Invalid reasoning includes:
For each invalid line, flag it with Violates: missing-why. The rewrite either supplies the missing tie ("Because compliance requires data residency in EU, this DB offers per-region replicas") or deletes the line.
See references/audit-checklist.md for the full missing-why interrogation.
Across the ADR and its graph neighbors, look for:
supersedes: ["0004"] in its frontmatter; related/amended links are not valid YAML lists; or the only evidence of a relationship lives in a body line such as Related ADRs: [ADR-0004](0004-foo.md) or in the decision-log README.md index. Gray-matter-style parsers read frontmatter only — body-only links and index links do not produce graph edges for that parser family. Flag every prose-only relationship for promotion into frontmatter (supersedes / amends / relates-to) with zero-padded four-digit string IDs.supersedes, amends, or relates-to, but the body has no ## More Information section with a ### Relationships sub-section mirroring those relationships using the doc-master link-prefix vocabulary (Supersedes, Superseded by, Amends, Amended by, Related to). These ADRs are invisible to body-scanning parsers (ADR Manager and similar).### Relationships section listing ADR-to-ADR links, but the corresponding frontmatter relationship keys are empty or absent. These ADRs are invisible to gray-matter-style parsers (ADR Explorer and similar).supersedes: ["0004"] but body Supersedes [ADR-0005]). Flag the discrepancy and ask which is correct.See references/audit-checklist.md for the consistency probes.
Glob **/*.c4 and any likec4.config.*. Compare component names referenced in the ADR with the LikeC4 model:
Do not auto-update either. The architect picks which side is the source of truth.
Once all flags are resolved, apply the architect-approved edits in one edit pass:
[reviewed] stamps, or commentary in the file.accepted, do not edit the body — instead, draft a new ADR that supersedes it. The audit can recommend supersession but cannot perform it silently.superseded by note in the header, or adding a dated "Amendments" note at the bottom under an explicit heading. Anything else is a new ADR.references/audit-checklist.md — the full per-section probe set used in Phases 3-5../_shared/adr-is-not.md — the canonical rule set referenced in flag Violates: fieldsadr-drafting skill — for ADRs being drafted fresh, not auditedc4-model skill — for resolving LikeC4 drift surfaced in Phase 5development
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.