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Zero Framework Cognition Principles
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Based on SOC occupation classification
Use the local pr-watch CLI/daemon to watch GitHub pull request activity, wait for PR changes, and fetch normalized event deltas without manually managing webhook forwarding or event subscriptions.
One lifecycle for Lambda repos: choose a bd task, start work, land the PR, and watch GitHub via Dumbwaiter MCP until it merges.
Default Python stack for Lambda: uv + Astral tools, typed code, schemas, and Hypothesis.
How to use bd (beads) for issue tracking, ready work, status updates, and comments in this repo.
Provider-agnostic wait-for-change skill that uses the Dumbwaiter MCP server to wait on PR events (GitHub first) via wait.start/status/cancel/await, with progress notifications and durable state.
Default TypeScript stack for Lambda: pnpm workspaces + Turbo, strict TS, and sensible DX helpers.
| name | zfc |
| description | Zero Framework Cognition Principles |
Core Architecture Principle: This application is pure orchestration that delegates ALL reasoning to external AI. We build a “thin, safe, deterministic shell” around AI reasoning with strong guardrails and observability.
IO and Plumbing • Read/write files, list directories, parse JSON, serialize/deserialize • Persist to stores, watch events, index documents
Structural Safety Checks • Schema validation, required fields verification • Path traversal prevention, timeout enforcement, cancellation handling
Policy Enforcement • Budget caps, rate limits, confidence thresholds • “Don’t run without approval” gates
Mechanical Transforms • Parameter substitution (e.g., ${param} replacement) • Compilation • Formatting and rendering AI-provided data
State Management • Lifecycle tracking, progress monitoring • Mission journaling, escalation policy execution
Typed Error Handling • Use SDK-provided error classes (instanceof checks) • Avoid message parsing
Local Intelligence/Reasoning
Ranking/Scoring/Selection • Any algorithm that chooses among alternatives based on heuristics or weights
Plan/Composition/Scheduling • Decisions about dependencies, ordering, parallelization, retry policies
Semantic Analysis • Inferring complexity, scope, file dependencies • Determining “what should be done next”
Heuristic Classification • Keyword-based routing • Fallback decision trees • Domain-specific rules
Quality Judgment • Opinionated validation beyond structural safety • Recommendations like “test-first recommended”
The Correct Flow
Gather Raw Context (IO only) • User intent, project files, constraints, mission state
Call AI for Decisions • Classification, selection, composition • Ordering, validation, next steps
Validate Structure • Schema conformance • Safety checks • Policy enforcement
Execute Mechanically • Run AI’s decisions without modification
