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 skills add Samurai-goose/SPECTER-The-Illusive-Security-Protocol --skill indepth-recon-analysisgit clone --depth 1 https://github.com/Samurai-goose/SPECTER-The-Illusive-Security-ProtocolWrote 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/samurai-goose/specter-the-illusive-security-protocol/indepth-recon-analysis)<a href="https://agentmods.dev/skills/samurai-goose/specter-the-illusive-security-protocol/indepth-recon-analysis"><img src="https://agentmods.dev/badge/skills/samurai-goose/specter-the-illusive-security-protocol/indepth-recon-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/samurai-goose/specter-the-illusive-security-protocol/indepth-recon-analysis"><img src="https://agentmods.dev/badge/skills/samurai-goose/specter-the-illusive-security-protocol/indepth-recon-analysis.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00037 | $0.01772 |
| Opus 5 | $0.00018 | $0.00886 |
| Sonnet 5 | $0.00007 | $0.00354 |
| Haiku 4.5 | $0.00004 | $0.00177 |
Grade A, and why
indepth-recon-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 11d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
In-Depth Recon Analysis
Purpose
Systematically analyze reconnaissance data to map the full attack surface of a target. Identify exposed services, technology stacks, entry points, data flows, and potential weaknesses. Transform raw recon into actionable intelligence for downstream security skills.
Triggers
- New target authorized for assessment
- User provides recon data (subdomains, ports, endpoints, technology fingerprints)
- User requests attack surface mapping
- Pre-engagement scoping requires surface analysis
- Source code available for analysis of exposed functionality
Required Inputs
| Input | Description | Required |
|---|---|---|
governance_context |
Active engagement governance record | Yes |
target |
Target application, domain, or repository | Yes |
recon_data |
Any combination of: subdomain lists, port scans, HTTP responses, technology fingerprints, directory listings, source code, API documentation | At least one |
Workflow
-
Scope Verification — Confirm all recon targets are within authorized scope.
-
Asset Inventory — Catalog all discovered assets:
- Domains and subdomains
- IP addresses and open ports
- Web applications and endpoints
- API endpoints (REST, GraphQL, WebSocket, gRPC)
- Login/authentication pages
- File upload/download endpoints
- Admin/management interfaces
- Third-party integrations
-
Technology Fingerprinting — Identify:
- Web servers (nginx, Apache, IIS, etc.)
- Application frameworks (Express, Django, Rails, Spring, etc.)
- Frontend frameworks (React, Angular, Vue, etc.)
- CMS platforms (WordPress, Drupal, etc.)
- Database technologies (inferred from errors, headers, behavior)
- Cloud services (AWS, Azure, GCP indicators)
- CDN/WAF presence (Cloudflare, Akamai, etc.)
- Version numbers where observable
- AI/LLM indicators:
- AI chatbot or assistant interfaces (conversational UI patterns)
- API endpoints calling AI providers (network requests to
api.openai.com,api.anthropic.com,generativelanguage.googleapis.com, etc.) - Streaming response patterns (SSE with
data:lines, chunked AI output) - RAG/knowledge base indicators (vector search endpoints, embedding APIs)
- AI plugin or function-call patterns in API responses
- Client-side AI SDK inclusion (OpenAI JS SDK, LangChain browser build)
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.
- 11d ago First seen · 186 lines · 37 tokens per session scan A c5c8f6bfa32b
indepth-recon-analysis is a skill published in the GitHub repository Samurai-goose/SPECTER-The-Illusive-Security-Protocol (2 stars, last pushed 2mo ago), licensed MIT. It adds 37 tokens to every session and 1,772 once invoked, about $0.0002 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 2026-08-31.
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