Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/cyberkaida/reverse-engineering-assistant/deep-analysisnpx skills add cyberkaida/reverse-engineering-assistant --skill deep-analysisgit clone --depth 1 https://github.com/cyberkaida/reverse-engineering-assistantWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cyberkaida/reverse-engineering-assistant/deep-analysis)<a href="https://agentmods.dev/skills/cyberkaida/reverse-engineering-assistant/deep-analysis"><img src="https://agentmods.dev/badge/skills/cyberkaida/reverse-engineering-assistant/deep-analysis.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00098 | $0.04988 |
| Opus 5 | $0.00049 | $0.02494 |
| Sonnet 5 | $0.00020 | $0.00998 |
| Haiku 4.5 | $0.00010 | $0.00499 |
Grade A, and why
deep-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 608 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 608 lines · 98 tokens per session scan A 622a5ab0622e
deep-analysis is a skill published in the GitHub repository cyberkaida/reverse-engineering-assistant (821 stars, last pushed 9d ago), licensed Apache-2.0. It adds 98 tokens to every session and 4,988 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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