xinyin025/LangBot
1
0
Fork 0
mirror of https://github.com/langbot-app/LangBot.git synced 2026-06-18 03:34:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat(agent-runner): expose skill resources through host context

Browse Source
This commit is contained in:
huanghuoguoguo
2026-06-07 12:48:06 +08:00
parent a9a2c18719
commit fa7b1b53a6
20 changed files with 463 additions and 193 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/langbot/pkg/pipeline/preproc/preproc.py
+1 -53
View File
@@ -171,7 +171,6 @@ class PreProcessor(stage.PipelineStage):
config_schema.supports_skill_authoring(descriptor)
and getattr(self.ap, 'skill_service', None) is not None
)
inject_skill_context = config_schema.supports_skill_injection(descriptor)
llm_model = None
if uses_host_models:
primary_uuid, fallback_uuids = config_schema.extract_model_selection(descriptor, runner_config)
@@ -350,19 +349,7 @@ class PreProcessor(stage.PipelineStage):
query.prompt.messages = event_ctx.event.default_prompt
query.messages = event_ctx.event.prompt
# =========== Skill awareness for capable runners ===========
# The actual activation goes through the ``activate`` Tool Call so the
# LLM doesn't see full SKILL.md instructions until it commits to a
# skill (Claude Code's progressive disclosure). But the LLM still has
# to KNOW which skills exist to make that choice, so we:
# 1. resolve the pipeline's bound skills and stash them in
# ``query.variables['_pipeline_bound_skills']`` for downstream
# visibility checks (skill loader, native exec workdir);
# 2. inject a short ``Available Skills`` index (name + description
# only) into the system prompt. The contributor's original PR
# relied on this injection; without it the LLM never discovers
# the skills are there and just calls native tools instead.
if inject_skill_context and self.ap.skill_mgr:
if include_skill_authoring and getattr(self.ap, 'skill_mgr', None) is not None:
pipeline_data = await self.ap.pipeline_service.get_pipeline(query.pipeline_uuid)
extensions_prefs = (pipeline_data or {}).get('extensions_preferences', {})
enable_all_skills = extensions_prefs.get('enable_all_skills', True)
@@ -374,43 +361,4 @@ class PreProcessor(stage.PipelineStage):
query.variables['_pipeline_bound_skills'] = bound_skills
skill_addition = self.ap.skill_mgr.build_skill_aware_prompt_addition(
bound_skills=bound_skills,
)
if skill_addition:
# Append to the first system message; create one if the
# prompt has none. Handles both plain-string and
# content-element (list) message bodies.
if query.prompt.messages and query.prompt.messages[0].role == 'system':
head = query.prompt.messages[0]
if isinstance(head.content, str):
head.content = head.content + skill_addition
elif isinstance(head.content, list):
appended = False
for ce in head.content:
if getattr(ce, 'type', None) == 'text':
ce.text = (ce.text or '') + skill_addition
appended = True
break
if not appended:
head.content.append(provider_message.ContentElement(type='text', text=skill_addition))
else:
query.prompt.messages.insert(
0,
provider_message.Message(role='system', content=skill_addition.strip()),
)
self.ap.logger.debug(
f'Skill index injected into system prompt: '
f'pipeline={query.pipeline_uuid} '
f'bound_skills={bound_skills or "all"} '
f'loaded_skills={len(self.ap.skill_mgr.skills)}'
)
else:
self.ap.logger.debug(
f'No skills available for prompt injection: '
f'pipeline={query.pipeline_uuid} '
f'loaded_skills={len(self.ap.skill_mgr.skills)} '
f'bound_skills={bound_skills}'
)
return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)