@akshay_pachaar: self-evolving skills in Hermes agent. i found this to be the most powerful feature in Hermes. the agent doesn't just so…

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Summary

Hermes agent features self-evolving skills that save successful troubleshooting procedures as reusable skill files, automatically managed by a background Curator to merge or archive skills without data loss.

self-evolving skills in Hermes agent. i found this to be the most powerful feature in Hermes. the agent doesn't just solve problems, it remembers how it solved them. memory handles facts, and skills handle procedures. the difference matters because knowing that a Kubernetes pod crashed is useless without knowing the exact sequence of commands that fixed it. here's how the self-improvement loop works in six steps: 1. the agent encounters a problem 2. works through trial and error: 5+ tool calls, errors, retries 3. finds a working solution 4. calls 𝘀𝗸𝗶𝗹𝗹_𝗺𝗮𝗻𝗮𝗴𝗲(𝗰𝗿𝗲𝗮𝘁𝗲) to save the successful approach as a 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱 file 5. the skill is saved to ~/.hermes/skills/ 6. next session, the agent reads the skill and follows the proven procedure instead of rediscovering from scratch skill creation triggers automatically when the agent completes a complex task, hits errors and recovers, receives a correction from you, or discovers a non-trivial workflow. now here's the important part: without maintenance, agent-created skills can pile up fast. that's why Hermes ships with a Curator that runs in the background, merging overlapping skills, archiving unused ones, and never auto-deleting anything. worst case is archival, and rollback is one command. in this way every hard problem the agent solves once, it solves faster the next time. the agent compounds. i wrote a full deep dive covering hermes agent's self-evolving skills, three-tier memory, GEPA optimization, and setting up multiple specialized agents. the article is quoted below.
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Cached at: 05/18/26, 12:25 AM

self-evolving skills in Hermes agent.

i found this to be the most powerful feature in Hermes.

the agent doesn’t just solve problems, it remembers how it solved them.

memory handles facts, and skills handle procedures. the difference matters because knowing that a Kubernetes pod crashed is useless without knowing the exact sequence of commands that fixed it.

here’s how the self-improvement loop works in six steps:

  1. the agent encounters a problem

  2. works through trial and error: 5+ tool calls, errors, retries

  3. finds a working solution

  4. calls 𝘀𝗸𝗶𝗹𝗹_𝗺𝗮𝗻𝗮𝗴𝗲(𝗰𝗿𝗲𝗮𝘁𝗲) to save the successful approach as a 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱 file

  5. the skill is saved to ~/.hermes/skills/

  6. next session, the agent reads the skill and follows the proven procedure instead of rediscovering from scratch

skill creation triggers automatically when the agent completes a complex task, hits errors and recovers, receives a correction from you, or discovers a non-trivial workflow.

now here’s the important part:

without maintenance, agent-created skills can pile up fast. that’s why Hermes ships with a Curator that runs in the background, merging overlapping skills, archiving unused ones, and never auto-deleting anything. worst case is archival, and rollback is one command.

in this way every hard problem the agent solves once, it solves faster the next time. the agent compounds.

i wrote a full deep dive covering hermes agent’s self-evolving skills, three-tier memory, GEPA optimization, and setting up multiple specialized agents.

the article is quoted below.

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