devin-cursorrules
Configures AI coding agents with advanced multi-agent orchestration, persistent state management, and automated Python execution capabilities similar to Devin AI.
64
/ 100 · Grade D
Maturity: Initial
“I need to configure AI coding agents with sophisticated multi-agent coordination, persistent memory, and automated script execution capabilities for complex development tasks.”
Trust Score Breakdown
Eight weighted signals composing the aggregate trust score
Scheme v1.1 · Weights provisional · Consumer confirmations and uptime use pipeline-derived baselines.
Pipeline Completion
Stages executed during verification
Supply Chain
SBOM analysis and vulnerability assessment
Components
0
Direct deps
0
Transitive deps
0
Total vulns
0
Vulnerability breakdown
Format: N/A N/A · Generated: Mar 16, 2026
Security Scan
Cisco Skill Scanner — static, behavioral, and LLM analysis
Adversarial Testing
Structured attack patterns from the Fidensa adversarial taxonomy
Categories tested
4
Findings
6
Methodology
v1.0
Categories
Findings
The skill's self-evolution mechanism allows user corrections and feedback to be directly incorporated into the scratchpad.md file without explicit data boundary markers. The instructions state that 'Whenever you correct the AI, it can update its "lessons learned" in .cursorrules' and the scratchpad contains a 'Lessons' section that accumulates knowledge. User-provided corrections could contain instruction-like content that overrides the skill's behavior when the AI references these lessons in future interactions.
The skill includes web browsing and search engine capabilities through Playwright and DuckDuckGo integration. The instructions indicate that 'The AI automatically decides how and when to use them' based on user requests. User-provided search queries or URLs could contain shell metacharacters, additional parameters, or path traversal sequences that could alter tool behavior. For example, a user could provide search terms containing semicolons, backticks, or other shell metacharacters that might be passed unsanitized to the underlying tools.
The skill explicitly instructs the agent to update and maintain a 'lessons learned' section that persists beyond individual tasks. This creates a mechanism for information to influence future unrelated tasks through accumulated knowledge storage.
The skill describes a 'self-evolution' capability where the AI updates its behavior based on corrections. While presented as project-specific, the mechanism could potentially influence behavior on unrelated tasks if the stored lessons are broadly applicable.
The skill instructions contain multiple pip install commands for packages that are not listed in the declared dependencies. This includes cookiecutter, playwright, and other packages that are installed through commands in the tutorial and setup instructions.
The skill instructions direct users to clone an external GitHub repository using cookiecutter without pinning to a specific commit hash. The command 'cookiecutter gh:grapeot/devin.cursorrules --checkout template' references a mutable branch/tag rather than a specific commit, allowing the repository owner to modify the cloned content after the skill is published.
Behavioral Fingerprint
Runtime performance baseline for drift detection
Samples
8
Error rate
0.0%
Peak memory
— MB
Avg CPU
—%
Response time distribution
Output size distribution
Fingerprint v1.0 · Baseline: Mar 16, 2026 · Status: baseline
Interface
Skill triggers and instruction summary
Activation
This skill activates when setting up enhanced AI capabilities for Cursor/Windsurf IDE or GitHub Copilot to provide Devin-like functionality
This skill handles the configuration and setup of advanced agentic AI capabilities including automated planning, tool usage, and multi-agent collaboration
Does
Provides setup instructions for enhanced IDE AI capabilities
Configures automated planning and self-evolution features
Enables extended tool usage including web browsing and search
Sets up multi-agent collaboration with planner-executor architecture
Implements self-learning through lessons learned accumulation
Does not
Does not automatically install dependencies without user consent
Does not modify existing project files without explicit setup
Does not guarantee compatibility with all IDE versions
Scope & Permissions
What this capability can and cannot access — derived from pipeline analysis
yes
no
yes
yes
yes
yes
Known Failure Modes
Documented edge cases and recovery behaviors
when when API keys are not configured
then the agent provides setup instructions but cannot use external services
when when dependencies are missing
then the agent provides installation instructions and may fail to execute advanced features
when when IDE is not supported
then the agent provides alternative configuration options or manual setup instructions
Review Flags
8 flags · 0 blocking
Adversarial finding (prompt_injection_chains): The skill's self-evolution mechanism allows user corrections and feedback to be directly incorporated into the scratchpad.md file without explicit data boundary markers. The instructions state that 'Whenever you correct the AI, it can update its "lessons learned" in .cursorrules' and the scratchpad contains a 'Lessons' section that accumulates knowledge. User-provided corrections could contain instruction-like content that overrides the skill's behavior when the AI references these lessons in future interactions.
Adversarial finding (prompt_injection_chains): The skill includes web browsing and search engine capabilities through Playwright and DuckDuckGo integration. The instructions indicate that 'The AI automatically decides how and when to use them' based on user requests. User-provided search queries or URLs could contain shell metacharacters, additional parameters, or path traversal sequences that could alter tool behavior. For example, a user could provide search terms containing semicolons, backticks, or other shell metacharacters that might be passed unsanitized to the underlying tools.
Adversarial finding (context_poisoning): The skill explicitly instructs the agent to update and maintain a 'lessons learned' section that persists beyond individual tasks. This creates a mechanism for information to influence future unrelated tasks through accumulated knowledge storage.
Adversarial finding (context_poisoning): The skill describes a 'self-evolution' capability where the AI updates its behavior based on corrections. While presented as project-specific, the mechanism could potentially influence behavior on unrelated tasks if the stored lessons are broadly applicable.
Adversarial finding (dependency_confusion): The skill instructions contain multiple pip install commands for packages that are not listed in the declared dependencies. This includes cookiecutter, playwright, and other packages that are installed through commands in the tutorial and setup instructions.
Adversarial finding (dependency_confusion): The skill instructions direct users to clone an external GitHub repository using cookiecutter without pinning to a specific commit hash. The command 'cookiecutter gh:grapeot/devin.cursorrules --checkout template' references a mutable branch/tag rather than a specific commit, allowing the repository owner to modify the cloned content after the skill is published.
Description section was synthesized by LLM from stage data — verify accuracy
Publisher "grapeot" is not verified — first certification from this publisher
Signed Artifact
Certification provenance and verification metadata
Pipeline Artifacts
Raw data files from this certification run — downloadable for independent verification
contract.json
Full unsigned contract
stage1-ingest.json
Ingest stage output
stage2a-sbom.json
SBOM generation results
stage2a-vulns.json
Vulnerability scan results
stage2b-security.json
Security scan results
stage3a-functional.json
Functional test results
stage3b-adversarial.json
Adversarial test results
stage3c-fingerprint.json
Behavioral fingerprint
stage4-certify.json
Certification decision + trust score
stage3a-measurements.json
Raw functional test measurements
stage3b-measurements.json
Raw adversarial test measurements
run-log.json
Pipeline execution log
Files served from Supabase Storage. Not all files may be present for every certification.