5c03e07ded
Simpler story (slide decks unread -> three-bullet status email), proper Claude-chat look with avatars and bubbles on both sides. Canonical trigger prompt switched to English everywhere (homepage, architecture, both adapter generators rebuilt) - V7 consistency green, all checks pass. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PDKeXvpT6tENSvyQGLV1Uq
70 lines
2.9 KiB
Python
70 lines
2.9 KiB
Python
"""ChatGPT adapter scaffold — instruction + knowledge bundle per profession.
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Runnable without any OpenAI account: it only produces the upload artifacts.
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"""
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import os
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import shutil
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import sys
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A = os.path.dirname(os.path.abspath(__file__))
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BASE = os.path.dirname(os.path.dirname(A))
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K = os.path.join(BASE, "knowledge")
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sys.path.insert(0, os.path.join(K, "pipeline"))
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from se_compile import PROFESSIONS # noqa: E402
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TRIGGER_PROMPT = ("Save the key insight from this chat as a lesson learned "
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"in my SkillFactor repo.")
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INSTRUCTION = """You are the SkillFactor occupational skill for the
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profession "{TITLE}". You start with this profession's experience: use the
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uploaded knowledge files (competence explanations, curated practitioner
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Q&A, vocabulary) as your primary reference before answering from general
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knowledge. Retrieval cascade when the SkillFactor gateway is connected:
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community profile -> the user's org overlay -> active project lessons;
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more specific beats more general.
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Contributing knowledge back: offer to save a lesson ONLY when the chat
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solved something non-trivial, transferable, with a real insight. The user
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can always trigger it manually with:
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"{TRIGGER}"
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Then: propose up to TWO distillates (project distillate with customer
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reference -> private org repo; generalized anonymized distillate -> public
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community layer as pull request) and ask "Both, just one, or neither?".
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Always show the complete final text; only confirmed text is submitted —
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word for word. Check the target folder for similar lessons first and
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propose an update instead of a duplicate. Toward the community layer:
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no names, no company/customer/project references, roles instead of
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persons, only the transferable pattern. Submission is always a pull
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request via the propose_lesson action, never a direct commit.
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"""
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def main():
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dist = os.path.join(A, "dist")
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if os.path.isdir(dist):
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shutil.rmtree(dist)
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for prof in PROFESSIONS:
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src = os.path.join(K, "professions", prof["slug"])
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if not os.path.isdir(src):
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continue
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out = os.path.join(dist, prof["slug"])
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os.makedirs(os.path.join(out, "knowledge"), exist_ok=True)
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open(os.path.join(out, "instructions.md"), "w", encoding="utf-8",
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newline="\n").write(
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INSTRUCTION.replace("{TITLE}", prof["title"])
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.replace("{TRIGGER}", TRIGGER_PROMPT))
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for f in ("knowledge.md", "vocabulary.json"):
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p = os.path.join(src, f)
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if os.path.exists(p):
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shutil.copy(p, os.path.join(out, "knowledge", f))
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cdir = os.path.join(src, "competences")
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if os.path.isdir(cdir):
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for f in sorted(os.listdir(cdir))[:20]: # GPT knowledge cap
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shutil.copy(os.path.join(cdir, f),
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os.path.join(out, "knowledge", f))
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print(f"bundle: {prof['slug']}")
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if __name__ == "__main__":
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main()
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