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feat(skill): align skill system with Claude Code's Tool Call design

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- Replace text marker activation with `activate` tool (Tool Call mechanism)
- Replace 7 authoring tools with 2: `activate` + `register_skill`
- Add builtin skills loading from templates/skills/
- Add create-skill as first builtin skill
- Remove SKILL_ACTIVATION_MARKER and text detection methods
- Tool Result returns SKILL.md content (protects KV Cache)

This aligns with Claude Code's progressive disclosure pattern:
- Metadata (name+description) always visible in tool description
- SKILL.md body loaded on activate via Tool Call
- Bundled resources accessible through virtual path mapping

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
huanghuoguoguo
2026-05-13 21:15:39 +08:00
parent 4db0f20dc4
commit 7145447bcb
6 changed files with 364 additions and 738 deletions
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src/langbot/pkg/pipeline/preproc/preproc.py
+9 -37
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@@ -248,7 +248,9 @@ class PreProcessor(stage.PipelineStage):
query.prompt.messages = event_ctx.event.default_prompt
query.messages = event_ctx.event.prompt
# =========== Inject skill index into system prompt ===========
# =========== Store bound skills for runtime visibility checks ===========
# Skills are now activated through the `activate` tool (Tool Call mechanism),
# not through system prompt injection. This aligns with Claude Code's design.
if selected_runner == 'local-agent' and self.ap.skill_mgr:
# Get bound skills from pipeline extensions_preferences
pipeline_data = await self.ap.pipeline_service.get_pipeline(query.pipeline_uuid)
@@ -264,42 +266,12 @@ class PreProcessor(stage.PipelineStage):
# Store bound skills in query variables for runtime path visibility checks
query.variables['_pipeline_bound_skills'] = bound_skills
# Build skill awareness addition
skill_addition = self.ap.skill_mgr.build_skill_aware_prompt_addition(
pipeline_uuid=query.pipeline_uuid,
bound_skills=bound_skills,
loaded_count = len(self.ap.skill_mgr.skills)
self.ap.logger.debug(
f'Skills available for activate tool: '
f'pipeline={query.pipeline_uuid} '
f'loaded_skills={loaded_count} '
f'bound_skills={bound_skills or "all"}'
)
if skill_addition:
self.ap.logger.info(
f'Skill index injected into system prompt: '
f'pipeline={query.pipeline_uuid} '
f'bound_skills={bound_skills or "all"} '
f'available_skills=[{", ".join(s["name"] for s in self.ap.skill_mgr.skills.values() if bound_skills is None or s["name"] in bound_skills)}]'
)
# Append skill instruction to the first system message
if query.prompt.messages and query.prompt.messages[0].role == 'system':
if isinstance(query.prompt.messages[0].content, str):
query.prompt.messages[0].content += skill_addition
elif isinstance(query.prompt.messages[0].content, list):
# Handle content as list of ContentElements
for ce in query.prompt.messages[0].content:
if ce.type == 'text':
ce.text += skill_addition
break
else:
# Insert a new system message with skill instructions
query.prompt.messages.insert(
0,
provider_message.Message(role='system', content=skill_addition.strip()),
)
else:
loaded_count = len(self.ap.skill_mgr.skills)
self.ap.logger.debug(
f'No skills available for injection: '
f'pipeline={query.pipeline_uuid} '
f'loaded_skills={loaded_count} '
f'bound_skills={bound_skills}'
)
return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)