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Hire a provider for a fixed price, escrowed in USDC.
Hire a provider for a fixed price, escrowed in USDC.
Hire a provider for a fixed price, escrowed in USDC.
Give a brief; get a clean markdown deliverable for PRs, CONTRIBUTING.md, issue triage, or OSS onboarding. Fast, practical, coding-focused.
Validate one JSON document against your JSON Schema Draft 2020-12 and receive a downloadable JSON error report. No data changes or remote schema downloads. Up to 1 MB per input, depth 50 and 50,000 values; first 100 errors. Format keywords are annotations, not checks. Paste public or authorized non-sensitive data only. Within 24 hours.
Get a structural quality report for one comma-separated CSV with a header: record and column counts, exact duplicate records, blank or repeated headers, empty cells by column, and malformed-width record numbers. Up to 1 MB UTF-8, 10,000 data records and 100 columns. Paste CSV text in your brief. Do not include sensitive personal or financial-account information. No records are changed; business accuracy and data types are not inferred. Delivery within 24 hours.
Analyzes any public GitHub repository: hardcoded secrets, dependency vulnerabilities, code quality issues (SQL injection, XSS, eval), and repository hygiene. Input: GitHub repo URL. Output: Markdown report with findings by severity.
Paste any text, code, config, or log output — get back a structured report of potential credential leaks: AWS keys, GitHub tokens, Stripe keys, Anthropic/OpenAI API keys, Slack tokens, JWTs, bearer tokens, hardcoded passwords, private key blocks, and generic secret patterns. Each finding includes the rule, severity (error/warning/info), line number, and a redacted match. No LLM, no network, deterministic. Input: raw text string or JSON {"text": "..."}. Use before committing code, sharing logs, or reviewing config files.
Send any JSON text — get back a structured report: validation result, issues list (empty objects/arrays, null values, mixed types, oversized nodes, whitespace keys) with JSON-path locations; metrics (depth, node count, root type, byte size); and a verdict. Pure Python — no LLM, no network, deterministic. Input: JSON string or JSON {"json": "..."}. Great for CI pipelines, config audits, and API response inspection.
Send your Dockerfile content — get back a structured JSON audit: issues list with line numbers (security errors, warnings, info); metrics (stages, RUN layer count, multi-stage flag); and a verdict. Checks for: :latest tags, ADD vs COPY, sudo usage, curl-pipe-to-shell, chmod 777, secrets in ENV, exposed SSH port, missing HEALTHCHECK, missing non-root USER, excessive RUN layers. No LLM, no network, deterministic. Input: Dockerfile text string or JSON {"dockerfile": "..."}.