Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/millionco/react-doctor --skill fuzzAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
Fuzz rules
Use packages/fuzz after focused rule tests pass. Read its README.md for harness architecture and corpus setup.
Run the harness
Run the target rule first:
FUZZ_RULE=<rule-id> FUZZ_STRICT=1 FUZZ_ITERATIONS=500 nr fuzzUseful variants:
nr fuzz
FUZZ_RULE=<rule-id> FUZZ_SEED=42 nr fuzz
FUZZ_INVARIANTS=1 nr fuzz
FUZZ_CORPUS_DIR=<repository-directory> nr fuzz
FUZZ_RULE=<rule-id> FUZZ_PRINT_SILENT=1 nr fuzzConfirm that the target rule fires. A silent rule only exercises early exits. If it stays silent, add a triggering shape to packages/fuzz/src/snippet-pools.ts and rerun.
Use FUZZ_TAG=<tag> instead of FUZZ_RULE to exercise an entire registry cohort.
Recommended full sweep (e.g. before a rule-batch release), pointing the corpus at real checkouts. Ready-made corpora work on any machine — see the package README "Canonical corpora": bun scripts/sync-fuzz-corpus.ts materializes a pinned 48-repo sample (symlinking the RDE cache when present, cloning otherwise), and bun scripts/build-bench-corpus.ts extracts react-bench's diagnostic-dense RD-health target files:
cd packages/fuzz
bun scripts/sync-fuzz-corpus.ts
FUZZ_INVARIANTS=1 FUZZ_ITERATIONS=100 FUZZ_CORPUS_DIR=tmp/corpus-repos nr fuzzRun the direct false-positive check when a rule change affects common syntax:
cd packages/fuzz
bun scripts/hunt-false-positives.tsTriage findings
Reproducers live in packages/fuzz/tmp/fuzz-findings/.
- Crash: minimize the program, add a no-throw regression test, fix the rule, and replay the seed
- Slow case: profile the pathological shape, bound the walk, and keep the existing threshold
- Verdict drop: fix detection that depends on incidental syntax, then add the rule to the robustness gate when appropriate
- Invariant violation: decide whether the rule should react to the rewrite; fix and test unexpected changes
- False positive: add the valid program to the rule tests and fuzz regression corpus
Replay one finding:
FUZZ_RULE=<rule-id> FUZZ_SEED=<seed> FUZZ_ITERATIONS=1 nr fuzzPreserve false positives
For every confirmed false positive:
- Add a minimal fixture to
packages/fuzz/corpus/regressions/<rule-id>--<slug>.tsx. - Include the rule ID, weakness class, and source in the fixture header.
- Add a focused valid case to the rule test suite.
- Add a generator snippet when the existing pools cannot produce the weakness.
- Run
nr -C packages/fuzz testand replay the target rule.
Use a stable weakness name such as library-idiom, control-flow, wrapper-transparency, name-heuristic, alias-guard, cross-file, framework-gating, paren-shape, default-parameter, dynamic-computed, private-member, or copy-tracking.
Project-level dead-code and dependency findings belong in core tests, not the rule fuzzer.
Report results
Record the command, target-rule fire count, findings, replay seeds, corpus fixtures, and generator changes. Pass confirmed implementation findings to rule-validate.





