Collects multi-agent operation events into traceable evidence chains with vector indexes.
npx skills add https://github.com/anbeime/skill --skill antinet-provenance
Please help me install this Agent Skill. Skill: antinet-provenance Source: https://github.com/anbeime/skill/tree/main/skills/antinet-provenance Command: npx skills add https://github.com/anbeime/skill --skill antinet-provenance
Multi-agent systems often lack end-to-end observability, making it impossible to audit who did what, when, and why. This Skill records every dispatch, scan, parse, and card-generation event into a traceable evidence chain so agent runs become auditable and replayable.
Run the provenance skill with python scripts/run_provenance.py to execute the full agent pipeline and write the trace evidence chain to the examples output directory.
High-intent search queries and answers about installing and using this skill.
Emit structured events with actor, action, target, timestamp, and trace_id at each key operation node, then write them into an evidence chain store. This Skill chains events by trace_id and produces trace.jsonl plus a summary for audit panels.
Run python scripts/run_provenance.py from the skill directory. It triggers the full agent pipeline stages and writes the evidence chain output to examples/snse_survey/provenance/ as trace.jsonl and trace_summary.json.
Events are cached in a local SQLite database and asynchronously backfilled after recovery, so the main pipeline is never blocked. Malformed individual events are logged and discarded without breaking the rest of the chain.
Yes, the skill exposes a query interface supporting retrieval by trace_id, time window, or actor dimension. On query timeout it returns the most recent successful snapshot marked as stale.
It depends on all agents emitting events under a unified schema, so non-compliant producers create gaps in the chain. It also adds storage overhead for vector indexes and event logs in high-throughput systems.
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