agentleFS
annatchijovaannatchijova/SKILLS/intel-source-evaluation/SKILL.md

intel-source-evaluation

Skill9 starsChanged 4 months agoLicence not identifiedView on GitHub

Grade the source and the indicator before acting on either — because an indicator is not intelligence and a feed is not truth, and auto-acting on ungraded intel means blocking benign traffic, chasing decayed IOCs, and spending analyst trust on someone else's low-confidence guess. Use whenever threat intelligence arrives and something is about to act on it: "block all these IOCs", "we got a threat feed", "this indicator list from the ISAC", "is this intel reliable", "should we alert on this IOC", "the vendor says APT-X uses this", "add these to the blocklist", "how long is this IOC good for". Sibling of claim-provenance-discipline (intel is a claim that must carry its origin and confidence) and threat-attribution-restraint (the actor label attached to a feed is itself a graded claim); ranks by source reliability and indicator durability. It does not produce the intel or attribute the actor, and never auto-actions an ungraded feed as if a match were a verdict.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

The licence could not be identified — read it at the source. We count its rules but don't quote them. Read them on GitHub.

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.