Synthesizes research, focus, data sources, and analytics into a formal hunt blueprint. It organizes existing artifacts without adding new research.
Install
mkdir -p .claude/skills/hunt-blueprint-generation && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4584" && unzip -o skill.zip -d .claude/skills/hunt-blueprint-generation && rm skill.zipInstalls to .claude/skills/hunt-blueprint-generation
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Assemble a complete hunt blueprint by consolidating outputs from prior hunt planning skills into a single, structured plan for execution. Use this skill after system and tradecraft research, hunt focus definition, data source identification, and analytics generation have been completed. This skill is synthesis and packaging only and must not introduce new research, assumptions, or analytics.Key capabilities
- →Synthesizes planning artifacts
- →Normalizes blueprint structure
- →Consolidates hunt research and findings
- →Organizes data sources and analytics
How it works
Aggregates pre-existing planning documents into a pre-defined schema without adding external research.
Inputs & outputs
When to use hunt-blueprint-generation
- →Formatting a hunt plan after research
- →Organizing candidate data sources
- →Consolidating hypothesis and tradecraft findings
About this skill
Generate Hunt Blueprint
This skill produces a single, structured hunt blueprint that captures the full hunt planning trajectory in an execution-ready format.
It is executed after the following have been completed:
- System internals and adversary tradecraft research
- Hunt focus definition (structured hypothesis)
- Candidate data source identification
- Analytics generation
This skill is assembly and synthesis only. It preserves and organizes outputs from prior steps without adding new research, new evidence, or new analytic logic.
Workflow
- You MUST complete each step in order and MUST NOT proceed until the current step is complete.
- You MUST NOT read reference documents unless the current step explicitly instructs you to do so.
- You MUST NOT perform new web searches or introduce new research.
- You MUST NOT generate new analytics, detections, thresholds, or validation logic.
- You MUST only use planning artifacts produced by prior skills.
Step 1: Normalize Blueprint Inputs
Confirm that all required inputs are available to assemble the hunt blueprint.
Use available planning artifacts, which may include:
- Research summary (system internals and adversary tradecraft)
- Candidate abuse patterns
- Structured hunt hypothesis
- Candidate data source summary
- Analytics summary and per-analytic details
If any critical planning artifact is missing, request it before proceeding.
Do NOT read reference documents during this step.
This step is complete when all required inputs are available.
Step 2: Assemble Blueprint Content
Populate the hunt blueprint using the section structure and ordering defined in
references/hunt-blueprint-template.md.
- Preserve wording and intent from prior artifacts where possible.
- Summarize only to reduce redundancy and improve readability.
- Ensure consistency across:
- Hunt hypothesis and research context
- Research context and analytics intent
- Candidate data sources and schema grounding
- Explicitly capture assumptions, gaps, and planning notes.
Do NOT introduce new material or reinterpret prior outputs.
This step is complete when all blueprint sections are populated according to the template.
Step 3: Refine Blueprint Content (In-Memory)
Refine the assembled blueprint for clarity and readability without writing it to disk yet.
Focus on editorial improvements only:
- Improve wording for clarity and conciseness
- Normalize terminology across hypothesis, research, data sources, and analytics
- Ensure behavioral models are expressed using clear, graph-like statements
(entity → relationship → entity) - Improve table readability and layout where needed
You MAY:
- Rephrase sentences for clarity
- Improve behavioral model descriptions
- Adjust Markdown structure for readability
You MUST NOT:
- Add or remove sections
- Change analytic intent or behavioral logic
- Introduce new research, assumptions, or analytics
- Write the blueprint to disk
This step is complete when the blueprint reads clearly and consistently.
Step 4: Validate and Write Blueprint
Validate the refined blueprint and correct issues before writing the final file.
Confirm that:
- The hunt hypothesis aligns with research context and the selected attack pattern
- Candidate data sources plausibly support the modeled behaviors
- Each analytic consistently connects:
- Analytic intent
- Data sources
- Entities and behavioral model
- Schema grounding
- SQL-like query-style representation
- Behavioral models are readable and graph-like
- Markdown renders correctly with no formatting or encoding issues
- Assumptions and gaps are planning-level only
You MAY:
- Fix Markdown rendering issues (broken tables, malformed lists, encoding artifacts)
- Correct structural violations of the template
You MUST NOT:
- Reword content for clarity
- Change analytic reasoning or behavior models
- Introduce new research, assumptions, analytics, or execution logic
Once validated, write the blueprint to a Markdown file named after the hunt using lowercase
words separated by underscores
(for example, windows_registry_persistence_hunt.md).
This step is complete when the blueprint is written and ready for execution.
When not to use it
- →During the research or detection generation phase
- →If planning artifacts are incomplete
Prerequisites
Limitations
- →Strict order of operations
- →Cannot generate new analytic logic
- →Requires completed prerequisite inputs
How it compares
It acts as an assembly tool that preserves context while ensuring the plan is ready for execution.
Compared to similar skills
hunt-blueprint-generation side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| hunt-blueprint-generation (this skill) | 1 | 7mo | No flags | Intermediate |
| agent-security-manager | 3 | 6mo | No flags | Advanced |
| security-auditor | 5 | 4mo | No flags | Advanced |
| cosmos-vulnerability-scanner | 3 | 2mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by OTRF
View all by OTRF →You might also like
agent-security-manager
ruvnet
Agent skill for security-manager - invoke with $agent-security-manager
security-auditor
sickn33
Expert security auditor specializing in DevSecOps, comprehensive cybersecurity, and compliance frameworks. Masters vulnerability assessment, threat modeling, secure authentication (OAuth2/OIDC), OWASP standards, cloud security, and security automation. Handles DevSecOps integration, compliance (GDPR/HIPAA/SOC2), and incident response. Use PROACTIVELY for security audits, DevSecOps, or compliance implementation.
cosmos-vulnerability-scanner
trailofbits
Scans Cosmos SDK blockchains for 9 consensus-critical vulnerabilities including non-determinism, incorrect signers, ABCI panics, and rounding errors. Use when auditing Cosmos chains or CosmWasm contracts.
openrouter-data-privacy
jeremylongshore
Implement data privacy controls for OpenRouter requests. Use when handling PII or meeting compliance requirements. Trigger with phrases like 'openrouter privacy', 'openrouter pii', 'openrouter gdpr', 'openrouter data protection'.
prompt-guard
Orchestra-Research
Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.
token-integration-analyzer
trailofbits
Token integration and implementation analyzer based on Trail of Bits' token integration checklist. Analyzes token implementations for ERC20/ERC721 conformity, checks for 20+ weird token patterns, assesses contract composition and owner privileges, performs on-chain scarcity analysis, and evaluates how protocols handle non-standard tokens. Context-aware for both token implementations and token integrations.