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 plurigrid/asi --skill analyzing-campaign-attribution-evidencegit clone --depth 1 https://github.com/plurigrid/asiWrote 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/plurigrid/asi/analyzing-campaign-attribution-evidence)<a href="https://agentmods.dev/skills/plurigrid/asi/analyzing-campaign-attribution-evidence"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/analyzing-campaign-attribution-evidence/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/plurigrid/asi/analyzing-campaign-attribution-evidence"><img src="https://agentmods.dev/badge/skills/plurigrid/asi/analyzing-campaign-attribution-evidence.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 | $0.00038 | $0.01917 |
| Opus 5 | $0.00019 | $0.00958 |
| Sonnet 5 | $0.00008 | $0.00383 |
| Haiku 4.5 | $0.00004 | $0.00192 |
Grade A, and why
analyzing-campaign-attribution-evidence 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 9d 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.
This is a copy
89% identical to analyzing-campaign-attribution-evidence — 23 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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,networkxlibraries - 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
Attribution Evidence Categories
- Infrastructure Overlap: Shared C2 servers, domains, IP ranges, hosting providers
- TTP Consistency: Matching ATT&CK techniques and sub-techniques across campaigns
- Malware Code Similarity: Shared code bases, compilers, PDB paths, encryption routines
- Operational Patterns: Timing (working hours, time zones), targeting patterns, operational tempo
- Language Artifacts: Embedded strings, variable names, error messages in specific languages
- Victimology: Target sector, geography, and organizational profile consistency
Confidence Levels
- High Confidence: Multiple independent evidence categories converge on same actor
- Moderate Confidence: Several evidence categories match, some ambiguity remains
- Low Confidence: Limited evidence, possible false flags or shared tooling
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.
- 9d ago First seen · 225 lines · 38 tokens per session scan A a7f73f36b659
analyzing-campaign-attribution-evidence is a skill published in the GitHub repository plurigrid/asi (64 stars, last pushed 2mo ago), licensed MIT. It adds 38 tokens to every session and 1,917 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to analyzing-campaign-attribution-evidence, differing in 23 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-campaign-attribution-evidence
Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use…
analyzing-campaign-attribution-evidence
Use when 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 attr.
analyzing-campaign-attribution-evidence
A skill for analysing evidence to estimate which threat actor or organisation is responsible for a cyberattack. It uses structured methods such as the Diamond Model and Analysis of Competing Hypotheses.
analyzing-campaign-attribution-evidence
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 attr.
analyzing-campaign-attribution-evidence
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 attr.
analyzing-campaign-attribution-evidence
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 attr.