TraceDecay Dev: Inspecting Automation Cycles
SkillDocs & knowledgeTraceDecay Dev: Use when auditing TraceDecay automation loops, skipped runs, memory-curator/session-reflector/skill-writer output, apply policy, or run artifacts.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the TraceDecay Dev: Inspecting Automation Cycles skill
What this skill tells your AI
The instructions your AI receives, as published by scriptedalchemy/tracedecay in .claude/skills/inspecting-automation-cycles/SKILL.md and read by ahel’s review.
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
- Start with
tracedecay automation config getto identify enabled tasks, schedules, locks, profile paths, and apply policy. - List recent runs with
tracedecay automation runs list --limit 100; group by task and status before opening individual artifacts. - For failures or suspicious skips, open the relevant artifact with
tracedecay_automation_run_artifact_viewortracedecay tool automation_run_artifact_view --args .... - Inspect model-managed memory outcomes plus configured managed-skill review
queues:
tracedecay automation facts list, dashboard telemetry, andtracedecay_skill_list --state pending. - 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_viewcalls against one profile while automation may write usage ledgers. If a usage read reports a truncated JSON or EOF parse error, retry once aftertracedecay_skill_listsucceeds. - Do not read
.tracedecaydatabases 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.
Signals
- GitHub stars
- 73
- Forks
- 5
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
inspecting-automation-cycles- Source
- github.com/scriptedalchemy/tracedecay