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Get Started Free →Replay the graph diff of one episode — a single git commit or working-tree save — to inspect what it changed: added/modified/removed symbols and edges. Use when the user asks what one commit or save changed in the graph, why code looks this way, or wants to inspect implementation attempts, reversions, past reasoning, or abandoned approaches across commits and working-tree episodes. Do not use git log or Grep for graph-level episode diffs; Memtrace replays indexed episode records. Do NOT use for
.claude/skills/syncable-dev-memtrace-episode-replay/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 184% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 73% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 8% | 0% |
Replay the graph diff for one episode (a single git commit or working-tree save). Shows which nodes/edges were added, modified, or removed in that episode — not a multi-episode time-range narrative.
Use get_evolution(mode: "recent") to find episode IDs and timestamps, then drill into a specific episode with this tool.
| Approach | Parameters | |---|---| | Known episode UUID | episode_id | | Newest episode in repo | repo_id + episode_index: 0 | | Nth newest | repo_id + episode_index: N |
There is no from / to / include_working_tree on this tool.
| Param | Purpose | |---|---| | symbol | Scope to one symbol name | | file_path | Substring filter on record paths | | kind | e.g. "Function", "CALLS" | | mode | "graph_summary" for digest on huge commits | | compress | default true — collapse identical-hash modifications | | limit / cursor | Pagination per bucket (default limit 200) |
Full parameter spec for every Memtrace tool: references/mcp-parameters.md (bundled at the memtrace-skills plugin root).
From get_evolution:
json{ "repo_id": "memdb", "from": "7d ago", "mode": "recent", "limit": 20 }
Each entry has episode metadata including id (use as episode_id) and reference_time.
Or use episode_index: 0 for the newest episode without knowing the UUID.
get_episode_replayBy episode UUID:
json{ "episode_id": "550e8400-e29b-41d4-a716-446655440000", "symbol": "execute", "file_path": "src/order/service.rs", "kind": "Function", "compress": true, "limit": 200 }
Newest episode, summary first on large commits:
json{ "repo_id": "memdb", "episode_index": 0, "mode": "graph_summary" }
| Field | Meaning | |---|---| | found | false if episode missing — check _note | | totals | Counts: nodes_added/modified/removed, edges_* | | nodes_added[] etc. | Per-record diffs (paginated) | | page.next_cursor | More records remain — pass as cursor |
For symbol history across many episodes, use get_timeline instead:
json{ "repo_id": "memdb", "scope_path": "OrderService.execute", "file_path": "src/order/service.rs" }
get_episode_replay returns the buckets interpreted in step 3:
json{ "found": true, "totals": { "nodes_added": 3, "nodes_modified": 12, "nodes_removed": 1, "edges_added": 7, "edges_removed": 2 }, "nodes_added": [ /* per-record diffs, paginated per bucket */ ], "nodes_modified": [ /* … */ ], "edges_removed": [ /* … */ ], "page": { "next_cursor": 200 } }
| Mistake | Reality | |---------|---------| | Passing from / to time window | Not supported — one episode per call | | Expecting narrative_hint / attempted_and_reverted | Not in API — inspect added/modified/removed buckets | | Unfiltered replay on 10k-symbol commits | Use kind, file_path, symbol, or mode: "graph_summary" first | | Using this for "what changed last week?" or module summaries | Use memtrace-evolution — get_evolution(from=..., mode: recent) — to list episodes |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 4,192 | 4,812 | +15% | 1 | 1 | 0% | 707 | 2,006 | +184% | 0 | 0 | — |
case-02 | fail→fail | 5,199 | 5,050 | -3% | 1 | 1 | 0% | 811 | 1,317 | +62% | 0 | 0 | — |
case-03 | fail→fail | 10,784 | 7,387 | -32% | 1 | 1 | 0% | 744 | 1,826 | +145% | 0 | 0 | — |
case-04 | fail→pass | 8,494 | 2,446 | -71% | 1 | 1 | 0% | 1,703 | 1,471 | -14% | 0 | 0 | — |
case-05 | pass→pass | 7,594 | 2,763 | -64% | 1 | 1 | 0% | 1,509 | 1,527 | +1% | 0 | 0 | — |
case-06 | pass→pass | 6,394 | 3,455 | -46% | 1 | 1 | 0% | 1,347 | 1,714 | +27% | 0 | 0 | — |
case-07 | fail→pass | 5,284 | 3,377 | -36% | 1 | 1 | 0% | 974 | 1,682 | +73% | 0 | 0 | — |
case-16 | fail→pass | 6,817 | 2,480 | -64% | 1 | 1 | 0% | 941 | 1,454 | +55% | 0 | 0 | — |
case-08 | fail→fail | 8,512 | 3,395 | -60% | 1 | 1 | 0% | 1,494 | 1,611 | +8% | 0 | 0 | — |
case-09 | fail→pass | 6,883 | 1,933 | -72% | 1 | 1 | 0% | 1,202 | 1,300 | +8% | 0 | 0 | — |
case-10 | fail→pass | 8,820 | 3,236 | -63% | 1 | 1 | 0% | 1,647 | 1,666 | +1% | 0 | 0 | — |
case-11 | fail→pass | 7,332 | 2,109 | -71% | 1 | 1 | 0% | 1,275 | 1,486 | +17% | 0 | 0 | — |
case-12 | pass→pass | 10,555 | 3,041 | -71% | 1 | 1 | 0% | 1,894 | 1,655 | -13% | 0 | 0 | — |
case-13 | fail→pass | 10,873 | 2,431 | -78% | 1 | 1 | 0% | 1,882 | 1,518 | -19% | 0 | 0 | — |
case-14 | fail→pass | 7,291 | 1,966 | -73% | 1 | 1 | 0% | 1,135 | 1,251 | +10% | 0 | 0 | — |
case-15 | fail→pass | 8,057 | 1,519 | -81% | 1 | 1 | 0% | 1,294 | 1,264 | -2% | 0 | 0 | — |
case-17 | fail→pass | 21,024 | 2,655 | -87% | 1 | 1 | 0% | 3,446 | 1,423 | -59% | 0 | 0 | — |
case-18 | pass→pass | 9,657 | 5,550 | -43% | 1 | 1 | 0% | 1,471 | 1,966 | +34% | 0 | 0 | — |
case-19 | fail→fail | 6,221 | 2,920 | -53% | 1 | 1 | 0% | 991 | 1,528 | +54% | 0 | 0 | — |
case-20 | fail→pass | 5,196 | 2,967 | -43% | 1 | 1 | 0% | 892 | 1,642 | +84% | 0 | 0 | — |
case-21 | fail→pass | 7,627 | 3,053 | -60% | 1 | 1 | 0% | 1,239 | 1,582 | +28% | 0 | 0 | — |
case-22 | fail→pass | 8,387 | 4,328 | -48% | 1 | 1 | 0% | 1,551 | 1,807 | +17% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 20 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +64 percentage points is the difference between those two pass rates over the 20 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.