Pensions Intel

Pensions Legal Intelligence: CLI-First Research

Context-efficient access to UK pensions regulatory data via the apex-intel CLI. Use this skill when you need quick, targeted queries without loading multiple MCP servers.

When To Use

Trigger conditions:

• User asks about UK pensions obligations (semantic search)
• User needs compliance gap analysis ("what am I missing?")
• User needs legal citations for obligations
• User wants handbook/guidance on a topic
• User asks "what obligations relate to X?"

Prefer this over pensions-research skill when:

• Context window is constrained
• Query is targeted (not exploratory)
• User needs quick answers, not comprehensive research
• Working on multiple topics in one session

Keywords:

• "obligation", "search obligations", "find obligations"
• "gap analysis", "compliance gaps", "missing"
• "citation", "cite", "source", "legislation"
• "handbook", "guidance", "TPR guidance"

CLI Tool Location

~/Projects/pensions/apex-governance/tools/apex_intel.py

Run with: uv run apex_intel.py <command> [options]

Command Reference

search - Semantic Search

Find obligations using natural language:

uv run apex_intel.py search "trustee disclosure duties"
uv run apex_intel.py search "ESG investment governance" --scheme-type DB
uv run apex_intel.py search "funding requirements" --limit 5 --json

Options:

• --limit, -l (int): Maximum results (1-50, default: 10)
• --scheme-type, -s: Filter by scheme (DB, DC, Hybrid, PSPS, All)
• --type, -t: Filter by category (Governance, Reporting, etc.)
• --json: Output as JSON for agent consumption

detail - Full Obligation Details

Get complete obligation with enrichment:

uv run apex_intel.py detail TPR-GC-001
uv run apex_intel.py detail PA04-S227-001 --json

Returns:

• Core info (source act, section, duty holder, priority)
• Statutory provisions
• Case law references
• Related obligations
• Enforcement bodies
• Definitions

gaps - Compliance Gap Analysis

Identify missing obligations:

uv run apex_intel.py gaps --known TPR-GC-001,TPR-GC-002
uv run apex_intel.py gaps --known TPR-GC-001 --threshold 60 --priority High

Options:

• --known, -k (required): Comma-separated obligation IDs
• --threshold, -t: Similarity threshold (0-100, default: 50)
  • 60-70: Strict (many gaps)
  • 50: Balanced (recommended)
  • 30-40: Loose (few gaps)
• --limit, -l: Max gaps to return
• --priority, -p: Filter by priority (High, Medium, Low, Critical)

similar - Find Related Obligations

Find semantically similar obligations:

uv run apex_intel.py similar PA04-S227-001
uv run apex_intel.py similar TPR-GC-001 --limit 10 --json

handbook - TPR Handbook Search

Search handbook guidance:

uv run apex_intel.py handbook "integrated risk management"
uv run apex_intel.py handbook "ESG due diligence" --limit 10

cite - Glass Box Citations

Generate traceable citations:

uv run apex_intel.py cite TPR-GC-001
uv run apex_intel.py cite PA04-S227-001 --format full
uv run apex_intel.py cite TPR-GC-001 --format legal

Formats:

• short: ID and source act only
• full: Complete with implementing SIs
• legal: Formal legal citation

Workflow Patterns

Pattern 1: Quick Research

# 1. Search for relevant obligations
uv run apex_intel.py search "disclosure duties" --limit 5 --json

# 2. Get details on most relevant
uv run apex_intel.py detail <obligation_id> --json

Pattern 2: Gap Analysis

# 1. List obligations user knows about
# (from their input or previous queries)

# 2. Run gap analysis
uv run apex_intel.py gaps --known OB-001,OB-002,OB-003 --threshold 50 --json

# 3. Report gaps with citations

Pattern 3: Citation Chain

# 1. Get obligation details
uv run apex_intel.py detail TPR-GC-001 --json

# 2. Build citation chain from response
# Obligation → Source Act s.Section → Implementing SI → Enforcement Body

Pattern 4: Comprehensive Research

# 1. Semantic search
uv run apex_intel.py search "topic" --json

# 2. Get similar obligations for top result
uv run apex_intel.py similar <top_id> --json

# 3. Search handbook for guidance
uv run apex_intel.py handbook "topic" --json

# 4. Combine findings with citations

Query Strategy Selection

Output Format for Agents

Always use --json flag when parsing results programmatically:

import subprocess
import json

result = subprocess.run(
    ["uv", "run", "apex_intel.py", "search", query, "--json"],
    capture_output=True, text=True,
    cwd="~/Projects/pensions/apex-governance/tools"
)
data = json.loads(result.stdout)
obligations = data["results"]

Glass Box AI Principle

Every legal conclusion must be traceable:

Conclusion → Obligation ID → Statutory Provision → Legislation

When reporting findings, always include:

1. Obligation ID
2. Source legislation (Act + section)
3. Duty holder
4. TPR priority
5. Enforcement body (if applicable)

Never state legal positions without citation chains.

Context Efficiency

This skill uses ~1,200 tokens vs ~8,000+ tokens for full MCP stack.

Recommendation: Use CLI for 80% of queries, MCP for complex multi-hop exploration.

Prerequisites

Auto-start (default): The CLI will automatically start the API backend if not running.

Manual requirements:

• Backend venv at backend/.venv (created with uv venv --python 3.12 .venv)
• Dependencies installed

Optional:

• Vectorized API at localhost:5001 (for semantic search; falls back to keyword)
• Doppler for credentials: doppler run -- uv run apex_intel.py ...

Disable auto-start:

uv run apex_intel.py --no-auto-start search "query"
# or
APEX_AUTO_START=false uv run apex_intel.py search "query"