refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool
Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to
match the design: user-initiated rescan, no prompt-cache reset, no new
schema surface, no phantom user turn, and the next-turn note carries each
added/removed skill's 60-char description (not just its name).
Changes vs the original PR:
* Drop the in-process skills prompt-cache clear in reload_skills(). Skills
are invoked at runtime via /skill-name, skills_list, or skill_view —
they don't need to live in the system prompt for the model to use them.
Keeping the cache intact preserves prefix caching across the reload so
/reload-skills pays no cache-reset cost. (MCP has to break the cache
because tool schemas must be known at conversation start; skills do not.)
* Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from
tools/skills_tool.py, plus the four skills_reload enumerations in
toolsets.py. No new schema surface — agents can already see a freshly-
installed skill via skill_view / skills_list the moment it's on disk.
* Replace the phantom 'role: user' turn injection with a one-shot queued
note. CLI uses self._pending_skills_reload_note (same pattern as
_pending_model_switch_note, prepended to the next API call and cleared).
Gateway uses self._pending_skills_reload_notes[session_key]. The note
is prepended to the NEXT real user message in this session, so message
alternation stays intact and nothing out-of-band is persisted to the
transcript.
* reload_skills() now returns added/removed as
[{'name': str, 'description': str}, ...] (description truncated to 60
chars — matches the curator / gateway adapter budget). The injected
next-turn note formats each entry as 'name — description' so the model
can actually reason about which new skills to call without running
skills_list first.
* Only emit the note when the diff is non-empty. On empty diff, print
'No new skills detected' and do nothing else.
* Tests rewritten to cover the queue semantics, the description payload,
and a regression guard that the prompt-cache snapshot is preserved.
This commit is contained in:
teknium1
98
cli.py
98
cli.py
@@ -7503,11 +7503,17 @@ class HermesCLI:
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print(f" ❌ MCP reload failed: {e}")
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def _reload_skills(self) -> None:
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"""Reload skills: rescan ~/.hermes/skills/, clear prompt cache.
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"""Reload skills: rescan ~/.hermes/skills/ and queue a note for the
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next user turn.
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Mirrors the ``/reload-mcp`` UX. After rescanning, the system prompt
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for the next turn is rebuilt with the fresh skill list and any
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``/skill-name`` slash commands are picked up immediately.
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Skills don't need to live in the system prompt for the model to use
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them (they're invoked via ``/skill-name``, ``skills_list``, or
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``skill_view`` at runtime), so this does NOT clear the prompt cache.
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It rescans the slash-command map, prints the diff for the user, and
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— if any skills were added or removed — queues a one-shot note that
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gets prepended to the next user message. This preserves message
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alternation (no phantom user turn injected out of band) and keeps
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prompt caching intact.
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"""
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try:
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from agent.skill_commands import reload_skills
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@@ -7516,49 +7522,54 @@ class HermesCLI:
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print("🔄 Reloading skills...")
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result = reload_skills()
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added = result.get("added", [])
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removed = result.get("removed", [])
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added = result.get("added", []) # [{"name", "description"}, ...]
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removed = result.get("removed", []) # [{"name", "description"}, ...]
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total = result.get("total", 0)
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if added:
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print(f" ➕ Added: {', '.join(added)}")
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if removed:
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print(f" ➖ Removed: {', '.join(removed)}")
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if not added and not removed:
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print(" No changes detected.")
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print(" No new skills detected.")
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print(f" 📚 {total} skill(s) available")
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return
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def _fmt_line(item: dict) -> str:
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nm = item.get("name", "")
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desc = item.get("description", "")
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return f" - {nm}: {desc}" if desc else f" - {nm}"
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if added:
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print(" ➕ Added Skills:")
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for item in added:
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print(f" {_fmt_line(item)}")
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if removed:
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print(" ➖ Removed Skills:")
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for item in removed:
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print(f" {_fmt_line(item)}")
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print(f" 📚 {total} skill(s) available")
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# Inject a system-style note so the model sees the new skill
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# list on its next turn. Appended at the end of history to
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# preserve prompt-cache for the prefix.
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change_parts = []
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# Queue a one-shot note for the NEXT user turn. The CLI's agent
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# loop prepends ``_pending_skills_reload_note`` (if set) to the
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# API-call-local message at ~L8770, then clears it — same
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# pattern as ``_pending_model_switch_note``. Nothing is written
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# to conversation_history here, so message alternation stays
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# intact and no out-of-band user turn is persisted.
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#
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# Format matches how the system prompt renders pre-existing
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# skills (`` - name: description``) so the model reads the
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# diff in the same shape as its original skill catalog.
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sections = ["[USER INITIATED SKILLS RELOAD:"]
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if added:
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change_parts.append(f"Added skills: {', '.join(added)}")
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sections.append("")
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sections.append("Added Skills:")
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for item in added:
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sections.append(_fmt_line(item))
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if removed:
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change_parts.append(f"Removed skills: {', '.join(removed)}")
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if change_parts:
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change_detail = ". ".join(change_parts) + ". "
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self.conversation_history.append({
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"role": "user",
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"content": (
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f"[IMPORTANT: Skills have been reloaded. {change_detail}"
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f"{total} skill(s) now available. Use skills_list to "
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f"see the updated catalog.]"
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),
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})
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# Persist immediately so the session log reflects the
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# reload event.
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if self.agent is not None:
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try:
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self.agent._persist_session(
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self.conversation_history,
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self.conversation_history,
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)
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except Exception:
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pass # Best-effort
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print(f" ✅ Skill cache cleared")
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sections.append("")
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sections.append("Removed Skills:")
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for item in removed:
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sections.append(_fmt_line(item))
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sections.append("")
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sections.append("Use skills_list to see the updated catalog.]")
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self._pending_skills_reload_note = "\n".join(sections)
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except Exception as e:
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print(f" ❌ Skills reload failed: {e}")
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@@ -8771,6 +8782,13 @@ class HermesCLI:
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if _msn:
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agent_message = _msn + "\n\n" + agent_message
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self._pending_model_switch_note = None
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# Prepend pending /reload-skills note so the model sees which
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# skills were added/removed before handling this turn. Same
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# one-shot queue pattern as the model-switch note above.
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_srn = getattr(self, '_pending_skills_reload_note', None)
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if _srn:
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agent_message = _srn + "\n\n" + agent_message
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self._pending_skills_reload_note = None
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try:
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result = self.agent.run_conversation(
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user_message=agent_message,
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