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Author runnable skill bodies via OpenAI-compatible codegen model

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create_skill now writes a real predict/act body: the small decision model
still authors title + description (engine.generate), then a larger
OpenAI-compatible model (default qwen38-iq3s) writes the runnable code
against the SKILL.md contract. Bodies persist to data/skills/<cat>/<name>.py,
are hot-loaded via importlib, merged into the running tree, and the request
re-dispatches to the new leaf. The dashboard decision-flow view shows the
title/description with a writing badge while the body is being written.
Codegen failure degrades to a navigable stub.
This commit is contained in:
Denton Social
2026-09-24 00:36:35 -05:00
parent 789ed4ae25
commit 440e49e76e
14 changed files with 910 additions and 18 deletions
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semif_agent/scheduler.py
+91 -8
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@@ -9,6 +9,7 @@ from __future__ import annotations
from dataclasses import dataclass, field
from .codegen import CodegenClient, CodegenError, generate_skill_body
from .decisions import DecisionRequest, Option, Request
from .engine import SemIfEngine
from .llm import LLMClient
@@ -21,12 +22,15 @@ from .skills import (
CreateCategory,
CreateSkill,
Skill,
SkillBodyStore,
build_skills,
build_tree,
compose_state,
generate_category,
generate_skill,
materialize_skill,
merge_registry,
merge_skill_bodies,
navigate,
)
from .trace import TraceLog
@@ -55,6 +59,7 @@ class DispatchResult:
summary: str
skill: str | None = None
decisions_logged: int = 0
body_written: bool = False
class Scheduler:
@@ -67,6 +72,7 @@ class Scheduler:
tau: float = 0.6,
max_reentries: int = 3,
trace: TraceLog | None = None,
codegen: CodegenClient | None = None,
):
self.engine = engine
self.llm = llm
@@ -75,6 +81,7 @@ class Scheduler:
self.config = config
self.tau = tau
self.max_reentries = max_reentries
self.codegen = codegen
self.queue = UrgencyQueue(
max_size=int(config.get("queue", {}).get("max_size", 100)),
age_rate=float(config.get("queue", {}).get("age_rate", 0.0)),
@@ -82,7 +89,9 @@ class Scheduler:
self.skills = build_skills(config)
self.tree = build_tree(self.skills)
self.registry = CategoryRegistry(config.get("category_registry", "data/categories.json"))
self.body_store = SkillBodyStore(config.get("skill_bodies", "data/skills"))
merge_registry(self.tree, self.registry.read())
merge_skill_bodies(self.tree, self.body_store, self.registry.read())
self.ctx = ActionContext(engine=self.engine, config=config)
self.runner = SkillRunner(self.ctx, self.llm, self.log)
self.current: Process | None = None
@@ -210,22 +219,32 @@ class Scheduler:
# ---- dispatch ----
def _dispatch(self, request: Request) -> DispatchResult:
def _dispatch(self, request: Request, _depth: int = 0) -> DispatchResult:
navigation = navigate(self.engine, self.log, self.trace, request, self.tree)
if isinstance(navigation, CreateCategory):
return self._create_category(request)
if isinstance(navigation, CreateSkill):
return self._create_skill(request, navigation.category)
outcome = self.runner.run(navigation, request)
created = self._create_skill(request, navigation.category)
if created.kind == "create_skill" and created.body_written and _depth < self.max_reentries:
requeued = _requeue(request, request.text)
self.trace.append(
"requeued", request.id, text=request.text, reason="skill created"
)
return self._dispatch(requeued, _depth=_depth + 1)
return created
return self._run_skill(navigation, request)
def _run_skill(self, skill: Skill, request: Request) -> DispatchResult:
outcome = self.runner.run(skill, request)
if outcome.error:
self.trace.append(
"error", request.id, skill=navigation.name, message=outcome.error
"error", request.id, skill=skill.name, message=outcome.error
)
return DispatchResult(kind="error", summary=f"skill error: {outcome.error}")
self.trace.append(
"assessed",
request.id,
skill=navigation.name,
skill=skill.name,
success=outcome.success,
summary=outcome.summary,
updated_request=outcome.updated_request,
@@ -235,8 +254,8 @@ class Scheduler:
self.trace.append("requeued", request.id, text=outcome.updated_request)
return DispatchResult(
kind="ran",
summary=f"{navigation.name}: {'ok' if outcome.success else 'failed'} — {outcome.summary}",
skill=navigation.name,
summary=f"{skill.name}: {'ok' if outcome.success else 'failed'} — {outcome.summary}",
skill=skill.name,
decisions_logged=outcome.decisions_logged,
)
@@ -275,7 +294,14 @@ class Scheduler:
)
def _create_skill(self, request: Request, category: str) -> DispatchResult:
"""Author a new skill leaf stub with the decision model in generation mode."""
"""Author a new skill leaf with the decision model in generation mode.
The small model writes the title + description; a larger OpenAI-
compatible model then writes the runnable body against SKILL.md. The
stub is registered first so the leaf is navigable even if the body
write fails; a successful write is merged into the tree as a runnable
skill and the request re-dispatches to it.
"""
from .engine import EngineUnavailable
try:
@@ -296,21 +322,78 @@ class Scheduler:
kind="error",
summary=f"create_skill failed: {draft.name} already exists",
)
self.registry.register_skill(category, draft.name, draft.description)
self.tree.setdefault(category, []).append(
Skill(name=draft.name, category=category, description=draft.description)
)
self.trace.append(
"skill_writing",
request.id,
category=category,
skill=draft.name,
description=draft.description,
model=self.codegen.model if self.codegen else None,
)
if self.codegen is None:
self.trace.append(
"skill_created",
request.id,
category=category,
skill=draft.name,
description=draft.description,
body=None,
written=False,
)
return DispatchResult(
kind="create_skill",
summary=f"created stub {category}.{draft.name}: {draft.description} (no codegen configured)",
skill=draft.name,
)
try:
draft.code = generate_skill_body(
self.codegen, request, category, draft, self.tree
)
skill = materialize_skill(draft, category, self.body_store)
except (CodegenError, ValueError) as exc:
self.trace.append(
"error",
request.id,
phase="create_skill",
category=category,
message=f"skill body write failed: {exc}",
)
return DispatchResult(
kind="create_skill",
summary=f"created stub {category}.{draft.name}: {draft.description} (body write failed: {exc})",
skill=draft.name,
)
skills = self.tree.setdefault(category, [])
for index, existing in enumerate(skills):
if existing.name == draft.name:
skills[index] = skill
break
else:
skills.append(skill)
skills.sort(key=lambda s: s.name)
body_path = self.body_store.body_path(category, draft.name).as_posix()
self.trace.append(
"skill_created",
request.id,
category=category,
skill=draft.name,
description=draft.description,
body=body_path,
written=True,
)
return DispatchResult(
kind="create_skill",
summary=f"created skill {category}.{draft.name}: {draft.description}",
skill=draft.name,
body_written=True,
)
def status(self) -> str: