analyzing-campaign-attribution-evidence SKILL.md skillA
This file is byte-identical to the first copy the registry indexed. Same content hash, so the same checks give both the same grade.
analyzing-campaign-attribution-evidence from 26zl/cybersec-toolkit is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add 26zl/cybersec-toolkit/analyzing-campaign-attribution-evidence@v1.0Pins v1.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- 26zl/cybersec-toolkit
- Path
.claude/skills/analyzing-campaign-attribution-evidence/SKILL.md- Versions recorded
- 1
- Latest version
- v1.0, committed Jun 2, 2026
- Size
- 9,015 bytes
- Repository stars
- 87
- License
- MIT
Campaign attribution analysis involves systematically evaluating evidence.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Analyzing Campaign Attribution Evidence ## Overview Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attribution indicators using the Diamond Model and ACH (Analysis of Competing Hypotheses), analyzing infrastructure overlaps, TTP consistency, malware code similarities, operational timing patterns, and language artifacts to build confidence-weighted attribution assessments. ## When to Use - When investigating security incidents that require analyzing campaign attribution evidence - When building detection rules or threat hunting queries for this domain - When SOC analysts need structured procedures for this analysis type - When validating security monitoring coverage for related attack techniques ## Prerequisites - Python 3.9+ with `attackcti`, `stix2`, `networkx` libraries - Access to threat intelligence platforms (MISP, OpenCTI) - Understanding of Diamond Model of Intrusion Analysis - Familiarity with MITRE ATT&CK threat group profiles - Knowledge of malware analysis and infrastructure tracking techniques ## Key Concepts …
Read the whole file at its exact version.
How to install
mdr add 26zl/cybersec-toolkit/analyzing-campaign-attribution-evidence@sha256:85ea0b03bfdd1179The line at the top of this page pins the label v1.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_5zccz4tccjtul34m)
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Versions
The registry has recorded 1 version of this file, committed on Jun 2, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (9015 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
26zl/cybersec-toolkit · 87 stars · license MIT · pushed 2026-11-01 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_5zccz4tccjtul34m GET https://markdownregistry.com/api/v1/resolve?ref=26zl/cybersec-toolkit/analyzing-campaign-attribution-evidence GET https://markdownregistry.com/api/v1/blob/85ea0b03bfdd1179ce7db812f1afa98971598e0b619deb98758e8aead6f7c99d
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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