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 dandye/ai-runbooks --skill hunt-credential-accessgit clone --depth 1 https://github.com/dandye/ai-runbooksWrote 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/dandye/ai-runbooks/hunt-credential-access)<a href="https://agentmods.dev/skills/dandye/ai-runbooks/hunt-credential-access"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/hunt-credential-access/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/dandye/ai-runbooks/hunt-credential-access"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/hunt-credential-access.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 66 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
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 | $0.00043 | $0.01166 |
| Opus 5 | $0.00022 | $0.00583 |
| Sonnet 5 | $0.00009 | $0.00233 |
| Haiku 4.5 | $0.00004 | $0.00117 |
Grade A, and why
hunt-credential-access 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- skill-044 — 89% identical, 11 lines differ
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Credential Access TTP Hunt Skill
Proactively hunt for MITRE ATT&CK Credential Access techniques (T1003, T1555, etc.) based on threat intelligence or hypothesis.
Inputs
TECHNIQUE_IDS- Comma-separated MITRE technique IDs (e.g., "T1003.001,T1555.003")TIME_FRAME_HOURS- Lookback period (default: 72)- (Optional)
TARGET_SCOPE_QUERY- UDM query to narrow scope - (Optional)
HUNT_HYPOTHESIS- Reason for the hunt - (Optional)
HUNT_CASE_ID- SOAR case for tracking
Common Techniques
| Technique | Description |
|---|---|
| T1003.001 | LSASS Memory |
| T1003.002 | Security Account Manager |
| T1003.003 | NTDS |
| T1003.004 | LSA Secrets |
| T1003.005 | Cached Domain Credentials |
| T1003.006 | DCSync |
| T1555.001 | Keychain |
| T1555.003 | Credentials from Web Browsers |
| T1555.004 | Windows Credential Manager |
Workflow
Step 1: Research Techniques
For each technique in TECHNIQUE_IDS:
gti-mcp.get_threat_intel(query="Explain MITRE ATT&CK technique T1003.001")
Understand:
- What the technique does
- Common procedures/tools
- Detection methods
Step 2: Develop Hunt Queries
T1003.001 - LSASS Memory Access:
metadata.event_type = "PROCESS_LAUNCH" AND
target.process.file.full_path = "C:\\Windows\\System32\\lsass.exe"
Look for suspicious parent processes accessing lsass.exe.
T1003.001 - Known Dumping Tools:
metadata.event_type = "PROCESS_LAUNCH" AND
(principal.process.command_line CONTAINS "mimikatz" OR
principal.process.command_line CONTAINS "procdump" OR
principal.process.command_line CONTAINS "sekurlsa")
T1555.003 - Browser Credential Files:
metadata.event_type = "FILE_OPEN" AND
(target.file.full_path CONTAINS "Login Data" OR
target.file.full_path CONTAINS "Web Data" OR
target.file.full_path CONTAINS "cookies.sqlite") AND
principal.process.file.full_path NOT IN ("chrome.exe", "firefox.exe", "msedge.exe")
T1003.006 - DCSync:
metadata.event_type = "DOMAIN_CONTROLLER_REPLICATION" AND
principal.hostname NOT IN @known_domain_controllers
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.
- 12d ago First seen · 160 lines · 43 tokens per session scan A 8edd3232b612
hunt-credential-access is a skill published in the GitHub repository dandye/ai-runbooks (124 stars, last pushed 28d ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,166 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…