inspecting-automation-cycles

name	inspecting-automation-cycles
description	TraceDecay Dev: Use when auditing TraceDecay automation loops, skipped runs, memory-curator/session-reflector/skill-writer output, apply policy, or run artifacts.

TraceDecay Dev: Inspecting Automation Cycles

TraceDecay automation is a loop, not a single artifact: config schedules jobs, runs produce artifacts, dashboards expose outcomes/telemetry, and usage analytics prove whether generated output was adopted.

Workflow

1. Start with tracedecay automation config get to identify enabled tasks, schedules, locks, profile paths, and apply policy.
2. List recent runs with tracedecay automation runs list --limit 100; group by task and status before opening individual artifacts.
3. For failures or suspicious skips, open the relevant artifact with tracedecay_automation_run_artifact_view or tracedecay tool automation_run_artifact_view --args ....
4. Inspect model-managed memory outcomes plus configured managed-skill review queues: tracedecay automation facts list, dashboard telemetry, and tracedecay_skill_list --state pending.
5. Check adoption evidence: tracedecay analytics diagnostics --all --no-sync, tracedecay sessions search "mcp__tracedecay" --provider all, and managed skill usage counts.

Reading Results

Signal	Meaning	Next step
scheduler_interval_not_elapsed	Healthy throttling	Count only, do not fix.
scheduler_lock_active	Another run owns the loop	Check age before calling stale.
no_new_session_activity	Nothing new to process	Verify transcript ingest if surprising.
validation_gate artifact	Mutation passed validation	Inspect apply-policy state or dashboard artifact.
Many fact proposal records	Inspect validation/apply telemetry	Use tracedecay:project-memory.
Active managed skills with zero use	Adoption telemetry gap	Use tracedecay:diagnosing-analytics.

Guardrails

• Prefer read-only inspection. Do not mutate fact records.
• Do not approve, reject, delete, or apply managed-skill drafts unless the user explicitly asked for mutation.
• Do not treat skipped runs as failures until grouped by skip reason and age.
• Avoid parallel tracedecay_skill_view calls against one profile while automation may write usage ledgers. If a usage read reports a truncated JSON or EOF parse error, retry once after tracedecay_skill_list succeeds.
• Do not read .tracedecay databases directly; use CLI, dashboard APIs, or MCP tools.

Deliverable

Report task/status counts, the exact run or artifact ids inspected, apply-policy state, adoption gaps, and the next concrete command for any mutation the user should choose.