This release adds 4 notable features for engineering teams evaluating rollout.
✓ No known CVEs patched in this version
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Summary
AI summaryMulti‑field secrets model added across vault, CLI, MCP tools, and desktop UI
Full changelog
What's Changed
- docs: fill placeholder documentation pages by @forest6511 in https://github.com/forest6511/secretctl/pull/102
- docs: fix documentation issues found by Codex review by @forest6511 in https://github.com/forest6511/secretctl/pull/103
- feat(vault): add multi-field secrets data model (Phase 2.5a) by @forest6511 in https://github.com/forest6511/secretctl/pull/105
- feat(cli): add multi-field support to set/get commands (Phase 2.5b) by @forest6511 in https://github.com/forest6511/secretctl/pull/108
- feat(mcp): add multi-field secret MCP tools (Phase 2.5c) by @forest6511 in https://github.com/forest6511/secretctl/pull/110
- docs: add Phase 2.5c MCP tools documentation by @forest6511 in https://github.com/forest6511/secretctl/pull/111
- feat(desktop): add multi-field secret editing (Phase 2.5d-2) by @forest6511 in https://github.com/forest6511/secretctl/pull/116
- feat(desktop): add template selection for secret creation (Phase 2.5d-3) by @forest6511 in https://github.com/forest6511/secretctl/pull/117
- docs: fix documentation inconsistencies from technical writer review by @forest6511 in https://github.com/forest6511/secretctl/pull/118
- docs: add well-known field names documentation (#119) by @forest6511 in https://github.com/forest6511/secretctl/pull/123
- feat(mcp): add field_count to secret_list output by @forest6511 in https://github.com/forest6511/secretctl/pull/124
- feat(mcp): extend secret_get_masked for multi-field secrets (#121) by @forest6511 in https://github.com/forest6511/secretctl/pull/125
- release: v0.7.0 - Phase 2.5 Multi-Field Secrets by @forest6511 in https://github.com/forest6511/secretctl/pull/126
Full Changelog: https://github.com/forest6511/secretctl/compare/v0.6.0...v0.7.0
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About forest6511/secretctl
AI-safe secrets manager with MCP integration. Run commands with credentials injected as environment variables - AI agents never see plaintext secrets. Features output sanitization, AES-256-GCM encryption, and Argon2id key derivation.
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