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 JesusConwellpy/secagent-skills --skill secagent-reasoninggit clone --depth 1 https://github.com/JesusConwellpy/secagent-skillsWrote 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/jesusconwellpy/secagent-skills/secagent-reasoning)<a href="https://agentmods.dev/skills/jesusconwellpy/secagent-skills/secagent-reasoning"><img src="https://agentmods.dev/badge/skills/jesusconwellpy/secagent-skills/secagent-reasoning/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/jesusconwellpy/secagent-skills/secagent-reasoning"><img src="https://agentmods.dev/badge/skills/jesusconwellpy/secagent-skills/secagent-reasoning.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 | $0.00032 | $0.01231 |
| Opus 5 | $0.00016 | $0.00616 |
| Sonnet 5 | $0.00006 | $0.00246 |
| Haiku 4.5 | $0.00003 | $0.00123 |
Grade A, and why
secagent-reasoning 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 8d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SecAgent Reasoning — Detective Engine
Loading this SKILL gives you structured security reasoning.
Reasoning Workflow
Input: tool output / agent report / scan result
Step 1: CLUE EXTRACTION → extract all clues from raw output
Step 2: CROSS-CORRELATION → find relationships between clues
Step 3: ANOMALY DETECTION → run applicable detectors
Step 4: HYPOTHESIS FORMATION → one testable hypothesis per anomaly
Step 5: EVIDENCE CHAIN VALIDATION → completeness, consistency, reliability
Output: structured reasoning report
Step 1: Clue Extraction
From any text, extract:
Regex patterns:
IP: \b(?:\d{1,3}\.){3}\d{1,3}\b
Domain: \b[a-zA-Z0-9-]+(?:\.[a-zA-Z]{2,})+\b
CVE: CVE-\d{4}-\d{4,}
Port: port: (\d{2,5})
Version: (\d+\.\d+(?:\.\d+)?)
Email: \b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}\b
URL: https?://[^\s]+
Semantic patterns:
Vuln type: SQLi / XSS / RCE / BoF / LFI / SSRF / CSRF / PrivEsc
Technique: ROP / ret2libc / heap spray / format string
Tool: nmap / burp / metasploit / sqlmap / gdb
Output format:
CLUE-{id}: {type}={value} | source={tool/agent} | confidence={0.0-1.0}
Step 2: Cross-Correlation
Relation types:
1. Spatial: same host/IP
CLUE-001(192.168.1.1:80) + CLUE-002(192.168.1.1:443) → same host
2. Causal: A enables B
Port open → Service running → Version fingerprinted → CVE matched
3. Temporal: same timeframe events
4. Type: same vulnerability class across different instances
Output format:
REL-{id}: {clue_a} --[{relation}]--> {clue_b}
Step 3: Anomaly Detection (12 detectors)
PortAnomaly: Non-standard ports
ServiceAnomaly: Outdated/vulnerable service versions
CVEAnomaly: Version matches known CVE
CredentialAnomaly: Default/weak credentials
ConfigAnomaly: Insecure configuration
PatchAnomaly: Known vulnerability unpatched
ChainAnomaly: Clues form attack chain (CRITICAL)
TrafficAnomaly: Unusual network patterns
FileAnomaly: Unusual filesystem activity
ProcessAnomaly: Unusual process behavior
LogAnomaly: Suspicious audit log entries
BehaviorAnomaly: Deviation from baseline
Output format:
ANOM-{id}: [{detector}] {description} | SEVERITY: {level}
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.
- 8d ago First seen · 162 lines · 32 tokens per session scan A 5feabea6df41
secagent-reasoning is a skill published in the GitHub repository JesusConwellpy/secagent-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 32 tokens to every session and 1,231 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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