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Add smart prompt enhancement system with 8 research-backed techniques
New feature: `multiagent enhance` applies proven prompt engineering techniques to any agent definition. Based on research from Anthropic, OpenAI, and academic papers (2025-2026). 8 composable enhancement blocks: - reasoning: Plan-and-reflect pattern (+20% on SWE-bench) - error_recovery: 5-level retry hierarchy (retry→rephrase→reroute→replan→escalate) - verification: Self-evaluation checklist before finalizing - confidence: Calibration to reduce hallucination 40-60% - tool_discipline: Priority-ordered tool selection heuristics - failure_modes: Explicit anti-patterns to avoid - context_management: Active context management for long tasks - information_priority: Source trust hierarchy to prevent hallucination 4 enhancement profiles: - category: Tuned per agent category (11 category-specific profiles) - all: All 8 enhancements - minimal: Just reasoning + verification - none: Base prompt only CLI: `multiagent enhance <agent> -p all -t claude-code` Pipeline: enhance → export works seamlessly 15 new tests (55 total, all passing) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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‎catalog/_enhancements/confidence.yaml‎

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name: confidence
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description: Confidence calibration to reduce hallucination by 40-60%
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category: accuracy
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prompt_block: |
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<confidence>
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Be calibrated in your confidence:
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- When confident, state conclusions directly
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- When uncertain, say so and offer alternatives
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- When you don't know, say "I don't know" rather than guessing
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- Distinguish between facts from tools/sources and your own inferences
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</confidence>

‎catalog/_enhancements/context_management.yaml‎

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name: context_management
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description: Active context management for long-running tasks
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category: efficiency
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prompt_block: |
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<context_management>
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Manage your working context actively:
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KEEP: Current goal, success criteria, completed steps, key findings
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SUMMARIZE: Replace raw data with concise summaries after processing
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DISCARD: Full tool outputs once relevant parts are captured
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Work on one task at a time. Complete it fully before moving to the next.
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If context grows large, periodically summarize progress.
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</context_management>

‎catalog/_enhancements/error_recovery.yaml‎

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name: error_recovery
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description: 5-level error recovery hierarchy for resilient agent behavior
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category: resilience
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prompt_block: |
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<error_recovery>
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When something fails, follow this hierarchy:
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LEVEL 0 — RETRY: Same call, wait briefly (transient errors like timeouts)
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LEVEL 1 — REPHRASE: Same intent, different parameters or query
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LEVEL 2 — REROUTE: Try an alternative tool or data source
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LEVEL 3 — REPLAN: Abandon current approach, design a new strategy
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LEVEL 4 — ESCALATE: Report what failed and why, request human guidance
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Never give up after a single failure. Never make up results when a tool fails.
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</error_recovery>

‎catalog/_enhancements/failure_modes.yaml‎

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name: failure_modes
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description: Common agent failure patterns to explicitly avoid
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category: safety
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prompt_block: |
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<failure_modes>
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Patterns to avoid:
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- Agreeing when you should push back on incorrect assumptions
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- Assuming context you don't have instead of investigating
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- Over-engineering simple problems or under-engineering complex ones
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- Stopping at analysis when implementation was requested
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- Producing plausible-sounding output without factual backing
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- Hard-coding values that only work for one specific case
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</failure_modes>

‎catalog/_enhancements/information_priority.yaml‎

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name: information_priority
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description: Clear hierarchy for information sources to prevent hallucination
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category: accuracy
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prompt_block: |
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<information_priority>
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Trust information in this order:
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1. Direct tool results and verified data (highest trust)
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2. User-provided context and requirements
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3. External search results with citations
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4. Your own knowledge (lowest trust — verify when possible)
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When sources conflict, prefer higher-trust sources and flag the discrepancy.
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</information_priority>

‎catalog/_enhancements/reasoning.yaml‎

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name: reasoning
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description: Enhanced reasoning with plan-and-reflect pattern (+20% on SWE-bench)
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category: cognitive
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prompt_block: |
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<reasoning>
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Before each action, plan your approach. After each result, reflect on whether
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it matches expectations. If not, adjust your strategy before proceeding.
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Keep going until the task is completely resolved. Do not stop at partial results.
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If you are not sure about something, use your tools to investigate — do NOT
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guess or make up an answer.
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</reasoning>

‎catalog/_enhancements/tool_discipline.yaml‎

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name: tool_discipline
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description: Optimized tool selection heuristics for efficient execution
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category: efficiency
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prompt_block: |
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<tool_discipline>
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Tool selection rules (priority order):
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1. Read local/cached data before searching externally
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2. Use the most specific tool available for the task
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3. Batch independent operations — call multiple tools in parallel when possible
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4. Never use destructive tools during analysis/read-only phases
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5. Validate tool results before building on them
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</tool_discipline>

‎catalog/_enhancements/verification.yaml‎

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name: verification
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description: Self-evaluation checklist before finalizing output
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category: quality
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prompt_block: |
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<verification>
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Before finalizing your response, verify:
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1. Does the output actually address the stated task?
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2. Is every claim backed by evidence or tool results?
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3. Are all requested items covered, or explicitly noted as blocked?
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4. Does the output format match what was requested?
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5. Are there contradictions or assumptions that need flagging?
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If any check fails, fix the issue before responding.
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</verification>

‎src/multiagent/__init__.py‎

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from multiagent.catalog import AgentDefinition, Catalog
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from multiagent.cost import CostEstimator
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from multiagent.enhance import enhance_agent
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from multiagent.export import export_agent
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from multiagent.patterns import patterns
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from multiagent.router import AgentRouter
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"AgentRouter",
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"Catalog",
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"CostEstimator",
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"enhance_agent",
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"export_agent",
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"patterns",
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]

‎src/multiagent/catalog.py‎

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if self._loaded:
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return
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for yaml_file in self._dir.rglob("*.yaml"):
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# Skip internal directories (enhancements, templates, etc.)
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if any(part.startswith("_") for part in yaml_file.relative_to(self._dir).parts):
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continue
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try:
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agent = AgentDefinition.from_yaml(yaml_file)
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self._agents[agent.full_name] = agent

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