A compounding operations stack for Anti Hunter + Anti Fund that combines:
- reliability guardrails,
- policy/rule governance,
- friction-to-backlog routing,
- approval-based learning,
- and daily execution planning.
- Source of truth:
playbooks/rules_registry.yaml - Rule IDs are referenced from prompts/jobs (thin prompts, less drift).
- Unified X worker + supporting jobs (treasury, shipping, backups, planning, learning, friction).
- Browser safety: mutex lock + single-tab policy + preflight checks + stop-on-thrash.
- Long-term memory:
MEMORY.md - Daily log:
memory/YYYY-MM-DD.md - Structured queues/logs:
memory/approval_queue.jsonlmemory/incidents.jsonlmemory/facts.json
- ETL:
scripts/run_friction_etl.py - Suppression:
scripts/friction_suppress_check.py - Routing:
scripts/triage_route.py --apply - Backlogs:
memory/projects/*.md
- Propose candidates: mistake -> correction -> rule
- Queue lifecycle via
scripts/approval_queue.py - Apply approved items into durable memory/rules
- Provenance tracked (
proposed_at,approved_at,applied_at,rule_hash, etc.)
- Build plan:
playbooks/daily_planning_system.md - Produce tasklist + gate + execution state
- Execute one task per run with explicit approval gate where required.
- Prefer one unified worker over duplicate jobs.
- Fail fast instead of retry-thrashing.
- Convert incidents into guardrails and measurable acceptance criteria.
- Run nightly validators before backup/state commits:
cron_consistency_check.pyrules_registry_check.pyincident_sla_check.pyincident_sla_enforce.py
- Skill path:
skills/antihunter-compounding-ops/SKILL.md
- Purpose:
- Operate and improve this architecture consistently with low drift and high reliability.