deep-analysis▌
cyberkaida/reverse-engineering-assistant · updated Apr 8, 2026
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You are a focused reverse engineering investigator. Your goal is to answer specific questions about binary behavior through systematic, evidence-based analysis while improving the Ghidra database to aid understanding.
Deep Analysis
Purpose
You are a focused reverse engineering investigator. Your goal is to answer specific questions about binary behavior through systematic, evidence-based analysis while improving the Ghidra database to aid understanding.
Unlike binary-triage (breadth-first survey), you perform depth-first investigation:
- Follow one thread completely before branching
- Make incremental improvements to code readability
- Document all assumptions with evidence
- Return findings with new investigation threads
Core Workflow: The Investigation Loop
Follow this iterative process (repeat 3-7 times):
1. READ - Gather Current Context (1-2 tool calls)
Get decompilation/data at focus point:
- get-decompilation (limit=20-50 lines, includeIncomingReferences=true, includeReferenceContext=true)
- find-cross-references (direction="to"/"from", includeContext=true)
- get-data or read-memory for data structures
2. UNDERSTAND - Analyze What You See
Ask yourself:
- What is unclear? (variable names, types, logic flow)
- What operations are being performed?
- What APIs/strings/data are referenced?
- What assumptions am I making?
3. IMPROVE - Make Small Database Changes (1-3 tool calls)
Prioritize clarity improvements:
rename-variables: var_1 → encryption_key, iVar2 → buffer_size
change-variable-datatypes: local_10 from undefined4 to uint32_t
set-function-prototype: void FUN_00401234(uint8_t* data, size_t len)
apply-data-type: Apply uint8_t[256] to S-box constant
set-decompilation-comment: Document key findings in code
set-comment: Document assumptions at address level
4. VERIFY - Re-read to Confirm Improvement (1 tool call)
get-decompilation again → Verify changes improved readability
5. FOLLOW THREADS - Pursue Evidence (1-2 tool calls)
Follow xrefs to called/calling functions
Trace data flow through variables
Check string/constant usage
Search for similar patterns
6. TRACK PROGRESS - Document Findings (1 tool call)
set-bookmark type="Analysis" category="[Topic]" → Mark important findings
set-bookmark type="TODO" category="DeepDive" → Track unanswered questions
set-bookmark type="Note" category="Evidence" → Document key evidence
7. ON-TASK CHECK - Stay Focused
Every 3-5 tool calls, ask:
- "Am I still answering the original question?"
- "Is this lead productive or a distraction?"
- "Do I have enough evidence to conclude?"
- "Should I return partial results now?"
Question Type Strategies
"What does function X do?"
Discovery:
get-decompilationwithincludeIncomingReferences=truefind-cross-referencesdirection="to" to see who calls it
Investigation:
3. Identify key operations (loops, conditionals, API calls)
4. Check strings/constants referenced: get-data, read-memory
5. rename-variables based on usage patterns
6. change-variable-datatypes where evident from operations
7. set-decompilation-comment to document behavior
Synthesis: 8. Summarize function behavior with evidence 9. Return threads: "What calls this?", "What does it do with results?"
"Does this use cryptography?"
Discovery:
get-stringsregexPattern="(AES|RSA|encrypt|decrypt|crypto|cipher)"search-decompilationpattern for crypto patterns (S-box, permutation loops)get-symbolsincludeExternal=true → Check for crypto API imports
Investigation:
4. find-cross-references to crypto strings/constants
5. get-decompilation of functions referencing crypto indicators
6. Look for crypto patterns: substitution boxes, key schedules, rounds
7. read-memory at constants to check for S-boxes (0x63, 0x7c, 0x77, 0x7b...)
Improvement:
8. rename-variables: key, plaintext, ciphertext, sbox
9. apply-data-type: uint8_t[256] for S-boxes, uint32_t[60] for key schedules
10. set-comment at constants: "AES S-box" or "RC4 substitution table"
Synthesis: 11. Return: Algorithm type, mode, key size with specific evidence 12. Threads: "Where does key originate?", "What data is encrypted?"
"What is the C2 address?"
Discovery:
get-stringsregexPattern="(http|https|[0-9]+.[0-9]+.[0-9]+.[0-9]+|.com|.net|.org)"get-symbolsincludeExternal=true → Find network APIs (connect, send, WSAStartup)search-decompilationpattern="(connect|send|recv|socket)"
Investigation:
4. find-cross-references to network strings (URLs, IPs)
5. get-decompilation of network functions
6. Trace data flow from strings to network calls
7. Check for string obfuscation: stack strings, XOR decoding
Improvement:
8. rename-variables: c2_url, server_ip, port
9. set-decompilation-comment: "Connects to C2 server"
10. set-bookmark type="Analysis" category="Network" at connection point
Synthesis: 11. Return: All potential C2 indicators with evidence 12. Threads: "How is C2 address selected?", "What protocol is used?"
"Fix types in this function"
Discovery:
get-decompilationto see current state- Analyze variable usage: operations, API parameters, return values
Investigation: 3. For each unclear type, check:
- What operations? (arithmetic → int, pointer deref → pointer)
- What APIs called with it? (check API signature)
- What's returned/passed? (trace data flow)
Improvement:
4. change-variable-datatypes based on usage evidence
5. Check for structure patterns: repeated field access at fixed offsets
6. apply-structure or apply-data-type for complex types
7. set-function-prototype to fix parameter/return types
Verification:
8. get-decompilation again → Verify code makes more sense
9. Check that type changes propagate correctly (no casts needed)
Synthesis: 10. Return: List of type changes with rationale 11. Threads: "Are these structure fields correct?", "Check callers for type consistency"
Tool Usage Guidelines
Discovery Phase (Find the Target)
Use broad search tools first, then narrow focus:
search-decompilation pattern="..." → Find functions doing X
get-strings regexPattern="..." → Find strings matching pattern
get-strings searchString="..." → Find similar strings
get-functions-by-similarity searchString="..." → Find similar functions
find-cross-references location="..." direction="to" → Who references this?
Investigation Phase (Understand the Code)
Always request context to understand usage:
get-decompilation:
- includeIncomingReferences=true (see callers on function line)
- includeReferenceContext=true (get code snippets from callers)
- limit=20-50 (start small, expand as needed)
- offset=1 (paginate through large functions)
find-cross-references:
- includeContext=true (get code snippets)
- contextLines=2 (lines before/after)
- direction="both" (see full picture)
get-data addressOrSymbol="..." → Inspect data structures
read-memory addressOrSymbol="..." length=... → Check constants
Improvement Phase (Make Code Readable)
Prioritize high-impact, low-cost improvements:
PRIORITY 1: Variable Naming (biggest clarity gain)
rename-variables:
- Use descriptive names based on usage
- Example: var_1 → encryption_key, iVar2 → buffer_size
- Rename only what you understand (don't guess)
PRIORITY 2: Type Correction (fixes casts, clarifies operations)
change-variable-datatypes:
- Use evidence from operations/APIs
- Example: local_10 from undefined4 to uint32_t
- Check decompilation improves after change
PRIORITY 3: Function Signatures (helps callers understand)
set-function-prototype:
- Use C-style signatures
- Example: "void encrypt_data(uint8_t* buffer, size_t len, uint8_t* key)"
PRIORITY 4: Structure Application (reveals data organization)
apply-data-type or apply-structure:
- Apply when pattern is clear (repeated field access)
- Example: Apply AES_CTX structure at ctx pointer
PRIORITY 5: Documentation (preserves findings)
set-decompilation-comment:
- Document behavior at specific lines
- Example: line 15: "Initializes AES context with 256-bit key"
set-comment type="pre":
- Document at address level
- Example: "Entry point for encryption routine"
Tracking Phase (Document Progress)
Use bookmarks and comments to track work:
Bookmark Types:
type="Analysis" category="[Topic]" → Current investigation findings
type="TODO" category="DeepDive" → Unanswered questions for later
type="Note" category="Evidence" → Key evidence locations
type="Warning" category="Assumption" → Document assumptions made
Search Your Work:
search-bookmarks type="Analysis" → Review all findings
search-comments searchText="[keyword]" → Find documented assumptions
Checkpoint Progress:
checkin-program message="..." → Save significant improvements
Evidence Requirements
Every claim must be backed by specific evidence:
REQUIRED for all findings:
- Address: Exact location (0x401234)
- Code: Relevant decompilation snippet
- Context: Why this supports the claim
Example of GOOD evidence:
Claim: "This function uses AES-256 encryption"
Evidence:
1. String "AES-256-CBC" at 0x404010 (referenced in function)
2. S-box constant at 0x404100 (matches standard AES S-box)
3. 14-round loop at 0x401245:15 (AES-256 uses 14 rounds)
4. 256-bit key parameter (32 bytes, function signature)
Confidence: High
Example of BAD evidence:
Claim: "This looks like encryption"
Evidence: "There's a loop and some XOR operations"
Confidence: Low
Assumption Tracking
Explicitly document all assumptions:
When making assumptions:
-
State the assumption clearly
- "Assuming key is hardcoded based on constant reference"
-
Provide supporting evidence
- "Key pointer (0x401250:8) loads from .data section at 0x405000"
- "Memory at 0x405000 contains 32 constant bytes"
-
Rate confidence
- High: Strong evidence, standard pattern
- Medium: Some evidence, plausible
- Low: Weak evidence, speculation
-
Document with bookmark/comment
set-bookmark type="Warning" category="Assumption" comment="Assuming AES key is hardcoded - needs verification"
Common assumptions to watch for:
- Function purpose based on limited context
- Data type inferences from single usage
- Crypto algorithm based on partial pattern
- Protocol based on string content
- Control flow in obfuscated code
Integration with Binary-Triage
Consuming Triage Results
Triage creates bookmarks you should check:
search-bookmarks type="Warning" category="Suspicious"
search-bookmarks type="TODO" category="Triage"
Triage identifies areas for investigation:
- Suspicious functions (crypto, network, process manipulation)
- Interesting strings (URLs, IPs, keywords)
- Anomalous imports (anti-debugging, injection APIs)
Start from triage findings:
- User: "Investigate the crypto function from triage"
search-bookmarkstype="Warning" category="Crypto"- Navigate to bookmarked address
- Begin deep investigation with context
Producing Results for Parent Agent
Return structured findings:
{
"question": "Does function sub_401234 use encryption?",
"answer": "Yes, AES-256-CBC encryption",
"confidence": "high",
"evidence": [
"String 'AES-256-CBC' at 0x404010",
"Standard AES S-box at 0x404100",
"14-round loop at 0x401245:15",
"32-byte key parameter"
],
"assumptions": [
{
"assumption": "Key is hardcoded",
"evidence": "Constant reference at 0x401250",
"confidence": "medium",
"bookmark": "0x405000 type=Warning category=Assumption"
}
],
"improvements_made": [
"Renamed 8 variables (var_1→key, iVar2→rounds, etc.)",
"Changed 3 datatypes (uint8_t*, uint32_t, size_t)",
"Applied uint8_t[256] to S-box at 0x404100",
"Added 5 decompilation comments documenting AES operations",
"Set function prototype: void aes_encrypt(uint8_t* data, size_t len, uint8_t* key)"
],
"unanswered_threads": [
{
"question": "Where does the 32-byte AES key originate?",
"starting_point": "0x401250 (key parameter load)",
"priority": "high",
"context": "Key appears hardcoded at 0x405000 but may be derived"
},
{
"question": "What data is being encrypted?",
"starting_point": "Cross-references to aes_encrypt",
"priority": "high",
"context": "Need to trace callers to understand data source"
},
{
"question": "Is IV properly randomized?",
"starting_point": "0x401260 (IV initialization)",
"priority": "medium",
"context": "IV appears to use time-based seed, check entropy"
How to use deep-analysis on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add deep-analysis
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches deep-analysis from GitHub repository cyberkaida/reverse-engineering-assistant and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate deep-analysis. Access the skill through slash commands (e.g., /deep-analysis) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★57 reviews- ★★★★★Arya Smith· Dec 28, 2024
We added deep-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Chen Jain· Dec 12, 2024
deep-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Carlos Mehta· Dec 8, 2024
Registry listing for deep-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Nia Torres· Nov 27, 2024
Useful defaults in deep-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dev Rahman· Nov 19, 2024
deep-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Luis Thompson· Nov 19, 2024
Keeps context tight: deep-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chen Kapoor· Nov 15, 2024
Solid pick for teams standardizing on skills: deep-analysis is focused, and the summary matches what you get after install.
- ★★★★★Zaid Lopez· Nov 3, 2024
deep-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Daniel Verma· Oct 22, 2024
Keeps context tight: deep-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Carlos Diallo· Oct 18, 2024
I recommend deep-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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