Sherlock is a monolithic Python CLI for username enumeration across social networks. For a given username it fans out concurrent HTTP probes to roughly 400 external sites (defined declaratively in sherlock_project/resources/data.json), then classifies each response as Claimed / Available / Unknown / Illegal / WAF using three detection modes: HTTP status code, presence/absence of a site-specific error message in the response body, or redirect behavior (redirects disabled, 2xx/3xx means claimed). Requests run in parallel via a requests-futures session capped at 20 workers; an optional per-site regex short-circuits illegal usernames without sending a request, and hardcoded fingerprint strings detect Cloudflare / PerimeterX / AWS WAF blocks. The CLI accepts one or more usernames, supports {?} wildcard expansion into `_`, `-`, `.` variants, and reports results to the terminal via a QueryNotify observer, with optional .txt, .csv, and .xlsx export. The system is fully data-driven: no per-site code exists. Each manifest entry declares the probe URL, error detection method, error signatures, optional headers/request method/JSON payload, and NSFW flag; a JSON Schema (data.schema.json) enforces conditional consistency (e.g., errorMsg required when errorType includes 'message'). By default the manifest is fetched live from https://data.sherlockproject.xyz, with an ephemeral false-positive exclusion list pulled from a dedicated GitHub branch; --local, --json, and --site flags override this behavior. Entry points are the `sherlock` console script, `python -m sherlock_project`, a Docker image entrypoint, and an Apify Actor (.actor/) that wraps the Docker image and pushes JSON results into an Apify dataset. Engineering strengths include the declarative manifest/schema design, a genuinely useful test suite (schema validation, live-target probe tests, randomized false-positive fuzzing, a Unicode regression test for issue #2730), and CI that runs a tox matrix across Python 3.10-3.14t on three OSes, validates only manifest entries modified by a PR, comments the validation summary back on the PR, and updates the exclusions list daily. Weaknesses: nearly all orchestration and output-format logic lives in a ~1,200-line sherlock.py; notify.py uses a module-global result counter; sites.py has mutable default arguments and a dead username_unclaimed parameter; the --print-found flag is always True (store_true combined with default=True); stem is an unused declared dependency; and the request layer relies on requests-futures, an effectively unmaintained library. The project is actively maintained and extremely popular (93k+ stars), and its health depends on hundreds of external sites remaining stable, hence the extensive removed-sites documentation and dedicated false-positive/false-negative issue templates. Technical drift is moderate rather than severe: the Python ^3.9 floor is now EOL, requests-futures is unmaintained, the WAF fingerprint catalog has not been extended since late 2024, and the PyPI readme still documents removed --tor flags.