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docs: release-parity sweep and evidence re-issue for v1.6
CHANGELOG [Unreleased] records the source-import hardening landing via #154 (deterministic missing detection, denial-guard supersession, generation-guarded finalization, keyset manifest paging, constant-time finalized check). Skills reference documents the fast retrieval profile and every engraphis_answer parameter; update-check docs state the opt-in default the code actually has; README gains ENGRAPHIS_UPDATE_CHECK/_URL rows, the new sync CLI surface, and the facade note; ARCHITECTURE_V3 qualifies schema-3 as historical; SYNC.md documents the shared-folder workspace_name residual risk and append-only ledger growth. Skill-asset manifest re-pinned; offline evidence artifact re-issued binding the edited eval/grounded.py (numbers unchanged) across all pinned surfaces.
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.claude-plugin/skill-assets.sha256

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94bfa06317a8fe6a6a7e204bb70c5abdc9e4bbc34d79dd6f8447a30140bc8b85 .claude-plugin/plugin.json
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055655db84af07561d002f0c69744313d8413c39f3e873f941f0fa0b1e76dc66 skills/engraphis-memory/references/CONVENTIONS.md
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62019760766ff472a76a0f81437898f39e3c1fe2631732b7b7733e50c1ad837f skills/engraphis-memory/references/SCOPING.md
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96c8e9b9cee1b3cb43c4bef9e48c57ed92af707f7f7a5d28d73b1ac247d2f0c6 skills/engraphis-memory/references/TOOLS.md
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449eb1428cac05e42d9ad0a7d816dc43f1acb67994f542935649d024c7b78623 skills/engraphis-memory/references/TOOLS.md
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0f98098df695b9a00dc78402911124ebf09a4a058f6c8bec2c6234ec61fac13a skills/engraphis-memory/SKILL.md

BENCHMARKS.md

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Every exact public aggregate retained below comes from the checked-in, public-safe
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[`offline-fixtures-v1.json`](docs/benchmark-evidence/offline-fixtures-v1.json) artifact. Its
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SHA-256 is
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`c3a74f1770ad3f868f55261ba11680e2dadca30167082ac2cb6669f9e3bdfad2`, also recorded in the
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`0f60b0868444f676fe14c5f94d7db2c475e22669930c4d760881d0842eaa6800`, also recorded in the
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adjacent `.sha256` file. The artifact contains no raw questions, answers, prompts, customer data,
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or per-record content fingerprints.
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CHANGELOG.md

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- Added `docs/GRAPH_PERFORMANCE.md` documenting the two graph presentation profiles,
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worker layout, progressive rendering, and the 20,000-node / 200,000-relation safety
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ceilings.
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- Source-import manifest paging now uses keyset (cursor) pagination instead of OFFSET,
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so concurrent writes during a source re-import can no longer skip or duplicate rows
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mid-scan (PR #154).
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### Fixed
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to register, instead of replaying the same broken asset response.
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- Existing Galaxy preferences migrate only the retired `48` orbital-separation default to `60`;
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deliberate custom values, including Gravity `0`, remain unchanged.
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- Source-import hardening lands via separate PR #154: deterministic missing-item detection
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now guards an unknown baseline instead of reporting spurious misses, denial-guard
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supersession binds digests computed from the parsed record rather than raw input,
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import-job finalization is generation-guarded so a stale worker cannot finalize over a
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newer attempt, and the finalized-state check completes in constant time.
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### Security
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- HTTP error responses in `vault.py` and `service.py` no longer echo user-controlled paths back
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to the client, preventing filesystem structure leakage (SEC-001).
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- Graph visibility SQL helpers now use parameterized queries instead of `repr(float)` string

README.md

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docs/ARCHITECTURE_V3.md

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# Engraphis v3 architecture
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This document is the design outline for the repo-graph, intent-native memory, resource-ingestion,
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retention-supervision, and privacy-receipt additions introduced with schema version 3.
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retention-supervision, and privacy-receipt additions introduced in the schema-3 era (the
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current schema version is 16).
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```mermaid
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flowchart LR

docs/MCP_TOOLS.md

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| Governance | `engraphis_promote` | Widens an explicitly approved memory's scope while preserving and linking its narrower history. |
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| Session | `engraphis_start_session` / `engraphis_end_session` | Starts or closes a work session. Exact retries are safe; `force_new=true` creates another session. |
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| Operations | `engraphis_stats` | Returns memory counts for health checks. |
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| Operations | `engraphis_check_update` | Refreshes the release cache and reports whether a newer version is available. |
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| Operations | `engraphis_check_update` | Refreshes the release cache and reports whether a newer version is available. Update checks are OFF unless `ENGRAPHIS_UPDATE_CHECK` is set to an affirmative value; `=0` keeps them off. |
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The classic recall, grounded, and answer tools (`engraphis_recall`,
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`engraphis_recall_grounded`, and the `engraphis_answer` alias) accept `planning="off"|"auto"`,

docs/SYNC.md

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Folder caps, oversize omissions, and snapshot races are observable incomplete failures rather than
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successful partial backups.
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Anyone who can write to the shared folder can also choose the target `workspace_name`, so
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content arriving from a peer you do not control should be treated as untrusted: it is
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quarantined under local `trusted: false` provenance until you review and approve it.
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Operator note: the `operation_receipts`, `events`, and `audit` tables in the local SQLite
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database grow append-only by design - rows are hash-chained, and pruning them would break
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chain verification. Watch their size in the database file (for example with
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`sqlite3 engraphis.db "SELECT count(*) FROM operation_receipts"`) when planning capacity;
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the supported path for long-lived installations is archiving or rotating the whole database,
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not deleting rows.
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## Merge semantics
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Sync exchanges bounded workspace snapshots and merges them deterministically. Existing

docs/benchmark-evidence/offline-fixtures-v1.json

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"contains_per_record_fingerprints": false
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},
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"suite": {
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"digest": "4d7e40607319cd4bf8caee3897f1e416dbe5b81998b37a7e4839409ee2923537",
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"digest": "4bfdfd6ccdf34ff7daa7441b8e788371c31985efe031b6520a265ef11f71ed1b",
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"digest_method": "sha256(canonical compact JSON mapping each sorted path to its file SHA-256)",
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"files": {
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"eval/chunking_eval.py": "a16544353940c0a8c40cea3b9932d3399b35ea5994b809b78f5dbe4a952c467f",
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"eval/datasets/codemem.jsonl": "341313023c22850a2e14f02742b571ad1deca824f886a1654a59541304c01f3c",
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"eval/datasets/longdoc.jsonl": "7f5ade95e1f283d0db8cf78e53ed8995d3534f847e616d2c0005fd8da37ac790",
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"eval/grounded.py": "a5dd62d10c079b0098917a4640315254c65a4f1d7d71d8c3a669f290a29277e5",
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"eval/grounded.py": "75ba96a4427508f2718323d283f7889fc90901a6176f12c4b3518f1ede5d96dd",
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"eval/performance.py": "e17ea78095e4e592717bc5d9d8e34d55fd98c3fd28d1a8104a20c227d4d619c9"
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}
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},
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docs/images/context-efficiency.svg

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