القائمة
Answer policy questions with strict citations. Refuses to answer without sources.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/FDU-INS/Insurance-Skills --skill policy-qaيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Complete architecture reference for the A3 insurance platform — file locations, data flow, conventions, microservice map, and fullstack patterns
Drafts U.S. commercial real estate access and indemnity (right-of-entry) agreements for pre-closing due diligence. Covers license grants, non-invasive vs invasive testing gates, insurance/endorsement requirements, indemnity with discovery carve-outs, restoration and lien remedies, confidentiality, and anti-indemnity guardrails. Trigger: access agreement, right of entry, due diligence access, Phase I/II, invasive testing, pre-PSA site inspection.
Guide actuarial analysis for insurance pricing. TRIGGERS - Use when user needs help with actuarial-analysis related tasks.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | policy-qa |
| version | 1.0.0 |
| owner | Platform AI Team |
| description | Answer policy questions with strict citations. Refuses to answer without sources. |
| dependencies | [{"mcp":"postgres"},{"mcp":"python"}] |
| capabilities | [{"citations_required":true},{"code_execution":"python"},{"refuse_without_source":true}] |
| guardrails | ["NEVER answer without citing sources","If no relevant sources found, say 'I cannot find relevant policy guidance'","Include section references and document URLs in citations","Flag uncertainty: if confidence < 0.8, say 'I'm not certain...'","No speculation or inference beyond what's in documents"] |
| inputs | [{"question":"Policy question from user"},{"top_k":"Number of sources to retrieve (default: 5)"},{"min_confidence":"Minimum confidence score (default: 0.8)"}] |
| workflow | [{"step":"Embed question using same model as policy corpus"},{"step":"Retrieve top_k similar chunks from pgvector"},{"step":"Rank by relevance score"},{"step":"If max score < min_confidence, refuse to answer"},{"step":"Generate answer citing specific sections and documents"},{"step":"Include all source URLs and section references"},{"step":"Log query, sources, answer in audit trail"}] |
| success_criteria | ["Answers include citations with section references","Refuses to answer when sources missing or low confidence","Citation precision ≥0.95 (citations actually support claims)","All interactions logged for audit"] |
Answer policy and SOP questions with strict source attribution. Refuses to answer without relevant sources. Designed for compliance-sensitive environments.
# Ask policy question
answer = policy_qa(
question="What is the approval workflow for journal entries over $100K?",
top_k=5,
min_confidence=0.8
)
from sentence_transformers import SentenceTransformer
def embed_question(question, model_name='sentence-transformers/all-MiniLM-L6-v2'):
"""Embed question using same model as policy corpus"""
model = SentenceTransformer(model_name)
embedding = model.encode(question)
return embedding.tolist()
import psycopg2
def retrieve_policy_sources(question_embedding, top_k=5):
"""Retrieve similar policy chunks from pgvector"""
conn = psycopg2.connect(os.environ['POSTGRES_URL'])
cursor = conn.cursor()
# Vector similarity search
query = """
SELECT
id,
text,
document_name,
section,
page_number,
url,
1 - (embedding <=> %s::vector) AS similarity
FROM policy_chunks
WHERE 1 - (embedding <=> %s::vector) > 0.3 -- Minimum similarity threshold
ORDER BY embedding <=> %s::vector
LIMIT %s
"""
cursor.execute(query, (question_embedding, question_embedding, question_embedding, top_k))
results = cursor.fetchall()
cursor.close()
conn.close()
return [{
'id': r[0],
'text': r[1],
'document_name': r[],
: r[],
: r[],
: r[],
: r[],
} r results]
def rank_sources(sources, question):
"""Re-rank sources by relevance using cross-encoder"""
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
# Score each source
pairs = [[question, s['text']] for s in sources]
scores = model.predict(pairs)
# Add scores to sources
for source, score in zip(sources, scores):
source['relevance_score'] = float(score)
# Sort by relevance
sources.sort(key=lambda x: x['relevance_score'], reverse=True)
return sources
def check_confidence(sources, min_confidence=0.8):
"""Check if top source meets confidence threshold"""
if not sources:
return False, "No relevant sources found"
top_score = sources[0]['relevance_score']
if top_score < min_confidence:
return False, f"Low confidence ({top_score:.2f} < {min_confidence})"
return True, None
def generate_answer_with_citations(question, sources):
"""Generate answer citing specific sources"""
# Build context from top sources
context = "\n\n".join([
f"[{i+1}] {s['document_name']}, Section: {s['section']}, Page: {s['page_number']}\n{s['text']}"
for i, s in enumerate(sources[:3]) # Use top 3 sources
])
# Prompt for answer generation
prompt = f"""
Answer the following question based ONLY on the provided policy documents.
You MUST cite your sources using [1], [2], [3] notation.
Question: {question}
Policy Documents:
{context}
Instructions:
- Answer the question using only information from the provided documents
- Cite sources inline using [1], [2], [3]
- If the documents don't contain the answer, say "I cannot find relevant policy guidance"
- Be specific about policy requirements, procedures, approvals
- Quote exact text when citing requirements
Answer:
"""
# Generate answer (using LLM)
answer = call_llm(prompt)
# Verify citations are present
if not any(f'[{i+1}]' in answer for i in range(len(sources[:3]))):
return "I cannot provide an answer without proper citations."
return answer
def call_llm(prompt):
"""Call LLM API (e.g., Claude, GPT, or local model)"""
# Implementation depends on LLM choice
pass
def format_response(answer, sources):
"""Format response with full source references"""
response = answer + "\n\n---\n\n**Sources:**\n\n"
for i, source in enumerate(sources[:3], start=1):
response += f"[{i}] **{source['document_name']}**\n"
response += f" Section: {source['section']}\n"
response += f" Page: {source['page_number']}\n"
response += f" URL: {source['url']}\n"
response += f" Relevance: {source['relevance_score']:.2f}\n\n"
return response
def log_policy_qa(user, question, sources, answer, refused=False):
"""Log policy Q&A for audit trail"""
import psycopg2
from datetime import datetime
conn = psycopg2.connect(os.environ['POSTGRES_URL'])
cursor = conn.cursor()
cursor.execute("""
INSERT INTO policy_qa_audit_log (
user_id, timestamp, question, sources, answer, refused, confidence_score
) VALUES (%s, %s, %s, %s, %s, %s, %s)
""", (
user,
datetime.now(),
question,
json.dumps([s['id'] for s in sources]),
answer if not refused else None,
refused,
sources[0]['relevance_score'] if sources else 0.0,
))
conn.commit()
cursor.close()
conn.close()
def policy_qa(question, top_k=5, min_confidence=0.8, user=None):
"""Answer policy question with strict citations"""
# 1. Embed question
question_embedding = embed_question(question)
# 2. Retrieve sources
sources = retrieve_policy_sources(question_embedding, top_k)
# 3. Re-rank
sources = rank_sources(sources, question)
# 4. Check confidence
passed, reason = check_confidence(sources, min_confidence)
if not passed:
response = f"I cannot answer this question. {reason}"
log_policy_qa(user, question, sources, response, refused=True)
return response
# 5. Generate answer
answer = generate_answer_with_citations(question, sources)
# 6. Format with sources
response = format_response(answer, sources)
# 7. Audit log
log_policy_qa(user, question, sources, answer, refused=False)
return response
Test citation precision:
# tests/finance/policy-qa.yaml
suite: policy-qa
thresholds:
citation_precision: 0.95
refuses_without_source: true
cases:
- id: ev-journal-approval
prompt: "What is the approval workflow for journal entries over $100K?"
expects:
- has_citations: true
- citation_count: [1, 2, 3] # 1-3 citations
- cites_correct_policy: true
- includes_source_urls: true
- id: ev-low-confidence-refusal
prompt: "What is the company's policy on flying cars?"
expects:
- refuses_to_answer: true
- mentions_no_sources: true
Ingest policy documents into pgvector:
def ingest_policy_document(pdf_path, document_name, url):
"""Ingest policy PDF into pgvector"""
# 1. Extract text from PDF
from pypdf import PdfReader
reader = PdfReader(pdf_path)
sections = []
for page_num, page in enumerate(reader.pages, start=1):
text = page.extract_text()
# Chunk by section headings or fixed size
chunks = chunk_text(text, chunk_size=500, overlap=100)
for chunk in chunks:
sections.append({
'text': chunk,
'document_name': document_name,
'page_number': page_num,
'section': detect_section(chunk), # Extract section heading
'url': url,
})
# 2. Embed all chunks
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
texts = [s['text'] for s in sections]
embeddings = model.encode(texts)
# 3. Store in pgvector
conn = psycopg2.connect(os.environ['POSTGRES_URL'])
cursor = conn.cursor()
for section, embedding in zip(sections, embeddings):
cursor.execute("""
INSERT INTO policy_chunks (
text, document_name, section, page_number, url, embedding
) VALUES (%s, %s, %s, %s, %s, %s)
""", (
section['text'],
section['document_name'],
section['section'],
section['page_number'],
section['url'],
embedding.tolist(),
))
conn.commit()
cursor.close()
conn.close()
CREATE TABLE policy_chunks (
id SERIAL PRIMARY KEY,
text TEXT NOT NULL,
document_name VARCHAR(255) NOT NULL,
section VARCHAR(255),
page_number INTEGER,
url TEXT,
embedding vector(384), -- MiniLM dimension
created_at TIMESTAMP DEFAULT NOW()
);
CREATE INDEX ON policy_chunks USING ivfflat (embedding vector_cosine_ops);
CREATE INDEX ON policy_chunks (document_name, section);
CREATE TABLE policy_qa_audit_log (
id SERIAL PRIMARY KEY,
user_id VARCHAR(255) NOT NULL,
timestamp TIMESTAMP NOT NULL,
question TEXT NOT NULL,
sources JSONB, -- Array of source IDs
answer TEXT,
refused BOOLEAN NOT NULL,
confidence_score FLOAT,
created_at TIMESTAMP DEFAULT NOW()
);
CREATE INDEX ON policy_qa_audit_log (user_id, timestamp);
CREATE INDEX ON policy_qa_audit_log (refused);
if not sources or sources[0]['relevance_score'] < min_confidence:
return "I cannot find relevant policy guidance on this topic. Please consult the full policy library or contact Compliance."
if sources[0]['relevance_score'] < 0.9:
answer = f"⚠️ **Moderate confidence** ({sources[0]['relevance_score']:.2f})\n\n{answer}"
Prompt includes:
- Do NOT infer or speculate beyond what's explicitly stated
- If the policy doesn't address this specific case, say so
- Quote exact text when citing requirements