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Mahanaicoach/google-maps-scraper-kit
Free, open-source Google Maps scraper for lead generation (Python, no API key, no Docker). Exports name, phone, email, website, rating and Instagram/Facebook/LinkedIn/TikTok/YouTube/X links to CSV. 100 places with emails in 44 s; --grid beats Google's 120 cap. MX email check, ad-pixel detection. Skill for Claude Code, Codex and Cursor.
AI provenance
100%
explicit
- Confidence
- 95%
- Coverage
- 100%
Estimated share of AI-generated code. Explicit attribution is a lower bound.
Code quality
56/100
weak
- Confidence
- 90%
- Coverage
- 100%
Complexity, duplication, lint density, tests, CI, docs and review process.
Security
73/100
adequate
- Confidence
- 85%
- Coverage
- 100%
Secrets, static analysis, vulnerable dependencies, CI and supply-chain posture.
- Repository: https://github.com/Mahanaicoach/google-maps-scraper-kit
- Commit:
446f231e66ba787465bb70dad2a416feec18dad3 - Generated: 2026-11-14T00:11:04+00:00
- Scorer 0.1.0, scoring prov-4+qual-2+sec-2, report schema v2
Scores
| Pillar | Score | Signal | Confidence | Coverage |
|---|---|---|---|---|
| AI provenance (estimated AI-generated share) | 100 | explicit | 95% | 100% |
| Code quality | 56 | weak | 90% | 100% |
| Security | 73 | adequate | 85% | 100% |
AI provenance
Explicit AI attribution on 100% of first-party code added; an estimated 100% including velocity signals.
- Explicitly attributed share (lower bound): 100.0%
- Unattributed share in AI-consistent velocity bursts: 0.0%
- Estimated AI-generated share: 100.0%
| AI tool | Attributed commits | Lines added |
|---|---|---|
| Claude Code | 126 | 12,918 |
- AI tooling configured in repo: claude-code, gemini, generic-agent
Current source lines last changed by AI-attributed commits: 6,235 of 6,235.
Too few lines in one group to compare finding densities.
- Stylometry index: 1.36 per 1,000 lines (narrated steps 0, placeholders 0, assistant phrasing 0, emoji 17). Reported as a signal, not scored.
| Month | Lines added | Attributed to AI |
|---|---|---|
| 2026-06 | 329 | 100% |
| 2026-10 | 10,037 | 100% |
| 2026-11 | 2,552 | 100% |
Evidence
- Claude Code: trailer 'Co-Authored-By: Claude' (446f231e66ba)
- Claude Code: on merged branch (d396197c86) (e23ac5fe6a0c)
- Claude Code: trailer 'Co-Authored-By: Claude' (c6a2e3208cbe)
- Claude Code: trailer 'Co-Authored-By: Claude' (66687d0cae9e)
- Claude Code: trailer 'Co-Authored-By: Claude' (793f01bedfd1)
- Claude Code: trailer 'Co-Authored-By: Claude' (38e50c855751)
- Claude Code: trailer 'Co-Authored-By: Claude' (51b40fbea158)
- Claude Code: trailer 'Co-Authored-By: Claude' (55fd9bff9572)
- Claude Code: trailer 'Co-Authored-By: Claude' (224da327f063)
- Claude Code: trailer 'Co-Authored-By: Claude' (8bacce4dbde9)
- Claude Code: trailer 'Co-Authored-By: Claude' (b4575271c429)
- Claude Code: trailer 'Co-Authored-By: Claude' (4e88cfb461c9)
- Claude Code: trailer 'Co-Authored-By: Claude' (55e77258d364)
- Claude Code: trailer 'Co-Authored-By: Claude' (4139d3d117d8)
- Claude Code: trailer 'Co-Authored-By: Claude' (126b223627ae)
AI provenance describes how code was produced; it is not a quality judgment. Unless attribution is explicit, these are signals consistent with AI generation, not proof. Squash/rebase workflows, imported code, and rewritten history reduce accuracy.
Code quality
Code quality is weak: test-to-source ratio 0.67, no CI test run detected; 29% of function code in complex or long functions; 2% duplicated lines. Weakest area: lint.
| Area | Score |
|---|---|
| Complexity (lizard) | 42 |
| Duplication | 87 |
| Code smells (Opengrep) | 9 |
| Tests and CI | 70 |
| Documentation | 80 |
| Type safety | 40 |
| Process (Scorecard review/maintenance) | n/a |
- Functions: 313; mean complexity 6.3, 90th percentile 12; 17 complex and 2 long functions
- Duplicated lines: 2.4% of 5,451 compared
- Tests: 4,204 test lines for 6,235 source lines (ratio 0.67); CI test commands: none detected; 82% of recent source commits also change tests
- Docs missing or thin: changelog
Most complex functions:
- main in scripts/scrape.py: complexity 123, 263 lines
- run_job in engine/jobs.py: complexity 115, 195 lines
- collect in engine/maps.py: complexity 41, 82 lines
- scrape_place in scripts/scrape.py: complexity 34, 71 lines
- import_kit1 in engine/jobs.py: complexity 29, 61 lines
Security
Security posture is adequate: 7 high-severity SAST findings; 6 high-severity workflow issues. Weakest area: ci actions.
| Area | Score |
|---|---|
| Secrets (gitleaks, tree + history) | 100 |
| Static analysis (Opengrep) | 62 |
| Vulnerable dependencies (OSV) | 100 |
| CI workflows (zizmor) | 28 |
| Posture (policy, updates, signing, lockfiles) | 40 |
| OpenSSF Scorecard | n/a |
- Posture checks passed: security policy, lockfiles; missing: dependency updates, signed commits
Top findings
| Severity | Finding | Location |
|---|---|---|
| high | opengrep/python.lang.security.audit.dangerous-subprocess-use-audit: Detected subprocess function 'Popen' without a static string. If this data can be controlled by a malicious actor, it may be an instance of command injection. Audit the use of this call to ensure it is not controllable by an external resource. You may consider using 'shlex.escape()'. [confidence low] |
engine/browser.py:314 |
| high | opengrep/python.sqlalchemy.security.sqlalchemy-execute-raw-query: Avoiding SQL string concatenation: untrusted input concatenated with raw SQL query can result in SQL Injection. In order to execute raw query safely, prepared statement should be used. SQLAlchemy provides TextualSQL to easily used prepared statement with named parameters. For complex SQL composition [confidence low] |
engine/jobs.py:312 |
| high | opengrep/python.sqlalchemy.security.sqlalchemy-execute-raw-query: Avoiding SQL string concatenation: untrusted input concatenated with raw SQL query can result in SQL Injection. In order to execute raw query safely, prepared statement should be used. SQLAlchemy provides TextualSQL to easily used prepared statement with named parameters. For complex SQL composition [confidence low] |
engine/jobs.py:317 |
| high | opengrep/python.sqlalchemy.security.sqlalchemy-execute-raw-query: Avoiding SQL string concatenation: untrusted input concatenated with raw SQL query can result in SQL Injection. In order to execute raw query safely, prepared statement should be used. SQLAlchemy provides TextualSQL to easily used prepared statement with named parameters. For complex SQL composition [confidence low] |
engine/jobs.py:330 |
| high | opengrep/python.lang.security.audit.dangerous-subprocess-use-audit: Detected subprocess function 'run' without a static string. If this data can be controlled by a malicious actor, it may be an instance of command injection. Audit the use of this call to ensure it is not controllable by an external resource. You may consider using 'shlex.escape()'. [confidence low] |
scripts/engine.py:114 |
| high | opengrep/python.lang.security.audit.dangerous-subprocess-use-audit: Detected subprocess function 'Popen' without a static string. If this data can be controlled by a malicious actor, it may be an instance of command injection. Audit the use of this call to ensure it is not controllable by an external resource. You may consider using 'shlex.escape()'. [confidence low] |
scripts/engine.py:272 |
| high | opengrep/python.lang.security.audit.dangerous-spawn-process-audit: Found dynamic content when spawning a process. This is dangerous if external data can reach this function call because it allows a malicious actor to execute commands. Ensure no external data reaches here. [confidence low] |
scripts/leads.py:41 |
| high | zizmor/unpinned-uses: unpinned action reference [confidence high] |
.github/workflows/engine-live.yml:32 |
| high | zizmor/unpinned-uses: unpinned action reference [confidence high] |
.github/workflows/engine-live.yml:33 |
| high | zizmor/unpinned-uses: unpinned action reference [confidence high] |
.github/workflows/engine-live.yml:105 |
| high | zizmor/unpinned-uses: unpinned action reference [confidence high] |
.github/workflows/tests.yml:29 |
| high | zizmor/unpinned-uses: unpinned action reference [confidence high] |
.github/workflows/tests.yml:32 |
| high | zizmor/unpinned-uses: unpinned action reference [confidence high] |
.github/workflows/tests.yml:46 |
| medium | opengrep/python.lang.security.audit.dynamic-urllib-use-detected: Detected a dynamic value being used with urllib. urllib supports 'file://' schemes, so a dynamic value controlled by a malicious actor may allow them to read arbitrary files. Audit uses of urllib calls to ensure user data cannot control the URLs, or consider using the 'requests' library instead. [confidence low] |
.github/live_reviews_check.py:12 |
| medium | opengrep/python.lang.security.audit.dynamic-urllib-use-detected: Detected a dynamic value being used with urllib. urllib supports 'file://' schemes, so a dynamic value controlled by a malicious actor may allow them to read arbitrary files. Audit uses of urllib calls to ensure user data cannot control the URLs, or consider using the 'requests' library instead. [confidence low] |
.github/live_reviews_check.py:24 |
| medium | opengrep/python.lang.security.audit.insecure-file-permissions: These permissions 0o700 are widely permissive and grant access to more people than may be necessary. A good default is 0o644 which gives read and write access to yourself and read access to everyone else. [confidence medium] |
engine/browser.py:64 |
| medium | opengrep/python.lang.security.audit.insecure-file-permissions: These permissions 0o755 are widely permissive and grant access to more people than may be necessary. A good default is 0o644 which gives read and write access to yourself and read access to everyone else. [confidence medium] |
engine/browser.py:167 |
| medium | opengrep/python.lang.security.insecure-hash-algorithm-sha1: Detected SHA1 hash algorithm which is considered insecure. SHA1 is not collision resistant and is therefore not suitable as a cryptographic signature. Use SHA256 or SHA3 instead. [confidence medium] |
engine/cdp.py:46 |
| medium | opengrep/python.lang.security.audit.regex_dos: Detected usage of re.compile with an inefficient regular expression. This can lead to regular expression denial of service, which can result in service down time. Instead, check all regexes or use safer alternatives such as pyre2. [confidence low] |
engine/jobs.py:887 |
| medium | opengrep/python.lang.security.audit.dynamic-urllib-use-detected: Detected a dynamic value being used with urllib. urllib supports 'file://' schemes, so a dynamic value controlled by a malicious actor may allow them to read arbitrary files. Audit uses of urllib calls to ensure user data cannot control the URLs, or consider using the 'requests' library instead. [confidence low] |
scripts/engine.py:375 |
| medium | opengrep/python.lang.security.audit.dynamic-urllib-use-detected: Detected a dynamic value being used with urllib. urllib supports 'file://' schemes, so a dynamic value controlled by a malicious actor may allow them to read arbitrary files. Audit uses of urllib calls to ensure user data cannot control the URLs, or consider using the 'requests' library instead. [confidence low] |
scripts/scrape.py:139 |
| medium | zizmor/artipacked: credential persistence through GitHub Actions artifacts [confidence low] |
.github/workflows/engine-live.yml:32 |
| medium | zizmor/artipacked: credential persistence through GitHub Actions artifacts [confidence low] |
.github/workflows/engine-live.yml:105 |
| medium | zizmor/artipacked: credential persistence through GitHub Actions artifacts [confidence low] |
.github/workflows/tests.yml:29 |
| medium | zizmor/artipacked: credential persistence through GitHub Actions artifacts [confidence low] |
.github/workflows/tests.yml:46 |
Static signals only: the scorer never runs the project's code. Secret findings are pattern matches that were not verified, and secret values are never stored.
Repository profile
- Source lines: 6,235; test lines: 4,204
| Language | Lines |
|---|---|
| Python | 10,256 |
| Shell | 183 |