[Feature Request] Auto-load relevant skills before task execution
Feature request: auto-suggest or auto-load relevant skills before task execution, since agents often forget to check installed skills.
Official issues about installing, loading, and managing skills. Sourced from the official GitHub repo; status derived from official labels; every entry links back to its source with a last-checked date.
Feature request: auto-suggest or auto-load relevant skills before task execution, since agents often forget to check installed skills.
The built-in himalaya skill documents an outdated --flag argument syntax for flag add/remove that fails on himalaya v1.2.0, which now requires positional arguments instead.
Field-testing the unbroker skill against a real subject surfaced structural issues: web_extract's DuckDuckGo backend can't fetch arbitrary pages, several brokers are blocked by anti-bot protection from datacenter IPs, and some brokers are missing reference JSON files.
An automated freshness probe detected the skills index in a degraded state (github: 0 < 30); this issue is auto-opened by a GitHub Actions workflow and will reopen until the underlying rebuild problem is fixed.
Skill sync leaves behind stale backup directories. A low-priority P3 cleanup issue that does not affect functionality.
Manually remove the stale .bak directories; skills keep working normally.
This issue proposes exploring integration of Microsoft's SkillOpt, a text-space optimizer for training reusable agent skills via trajectory-driven edits, to enable automated skill optimization in Hermes without modifying model weights.
The google-workspace skill only supports a single Google account due to a fixed token file path, forcing users with multiple accounts to route the second account through an external tool; the issue proposes an --account flag with per-account token files.
A discussion-style feature request raises governance concerns about Hermes' self-modifying skills: since skills are created and improved automatically, there is no built-in way to prove which version of a skill produced a given output or under what policy it ran.
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