reasoningbank-agentdb

name	reasoningbank-agentdb
description	ReasoningBank integrations that rely on AgentDB for memory and retrieval.
allowed-tools	Read, Write, Edit, Bash, Glob, Grep, Task, TodoWrite
model	sonnet
x-version	3.2.0
x-category	platforms
x-vcl-compliance	v3.1.1
x-cognitive-frames	["HON","MOR","COM","CLS","EVD","ASP","SPC"]

Purpose

Wire ReasoningBank workflows into AgentDB-backed retrieval with verification and safety notes.

Trigger Conditions

• Use this skill when: Need ReasoningBank tasks to persist or retrieve context via AgentDB.
• Reroute when: If ReasoningBank runs without external memory, use reasoningbank-intelligence.

Guardrails (Inherited from Skill-Forge + Prompt-Architect)

• Structure-first: every platform skill keeps SKILL.md, examples/, and tests/ populated; create resources/ and references/ as needed. Log any missing artifact and fill a placeholder before proceeding.
• Confidence ceilings are mandatory in outputs: inference/report 0.70, research 0.85, observation/definition 0.95. State as Confidence: X.XX (ceiling: TYPE Y.YY).
• English-only user-facing text; keep VCL markers internal. Do not leak internal notation.
• Adversarial validation is required before sign-off: boundary, failure, and COV checks with notes.
• MCP tagging for runs: WHO=reasoningbank-agentdb-{session}, WHY=skill-execution, namespace skills/platforms/reasoningbank-agentdb/{project}.

Execution Framework

1. Intent & Constraints — clarify task goal, inputs, success criteria, and risk limits; extract hard/soft/inferred constraints explicitly.
2. Plan & Docs — outline steps, needed examples/tests, and data contracts; confirm platform-specific policies.
3. Build & Optimize — apply platform playbook below; keep iterative checkpoints and diffs.
4. Validate — run adversarial tests, measure KPIs, and record evidence with ceilings.
5. Deliver & Hand off — summarize decisions, artifacts, and next actions; capture learnings for reuse.

Platform Playbook

• Workflow patterns:
  • Model prompts that log reasoning traces into AgentDB
  • Design retrieval plans that fetch prior proofs/steps
  • Validate recalled steps with confidence ceilings before reuse
• Anti-patterns to avoid: Using unverified traces directly, Leaving reasoning data unlabeled by case, Omitting confidence ceilings on recalled content
• Example executions:
  • Store reasoning traces and query them for similar proofs
  • Attach vector search to recall prior analyses in new tasks

Documentation & Artifacts

• SKILL.md (this file) is canonical; keep quick-reference notes in README.md if present.
• examples/ should hold runnable or narrative examples; tests/ should include validation steps or checklists.
• resources/ stores helper scripts/templates; references/ stores background links or research.
• Update metadata.json version if behavior meaningfully changes.

Verification Checklist

• Trigger matched and reroute considered
• Examples/tests present or stubbed with TODOs
• Constraints captured and confidence ceiling stated
• Validation evidence captured (boundary, failure, COV)
• MCP tags applied for this run

Confidence: 0.70 (ceiling: inference 0.70) - Standardized platform skill rewrite aligned with skill-forge + prompt-architect guardrails.