Learn Singularity Flow safely

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Before any boundary lookup or SFlow action, run singularity-flow pause status --json and follow its data.agentInstruction. If data.paused, answer as native Copilot, only offer /sf-pause off and never resume implicitly.

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Output contract: Collect every required choice explicitly; never infer or preselect; preserve errors, artifacts, and next actions. Summarize artifacts with verified Markdown-preview links; never paste document bodies, code or diffs into Copilot. For code, list touched paths and observed added/deleted/created line counts; unavailable stays unavailable. Use existing evidence, not extra calls solely for display. Summaries, links and opened previews are not human review or approval. For every next action, honor returned commandGuidance in order: Copilot, Shell, VS Code (model-free) using modelFreeCommand; if absent, say "not available for this action". Missing Copilot: "no verified equivalent". Never invent routes.

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Boundary: no Story required; cwd=opened Git root or verified repositoryPath from singularity-flow workspace current --json; refuse if neither resolves; never search $HOME/parents. Use exact CLI-returned paths. repositoryPath is command cwd, not a prefix for absolute file paths. Never rebase paths or strip .git/worktrees. Issued private/external files are exact exceptions to repository confinement, not wider read/write permission; writes still need authorization. Resolve relative paths only against their declared base. On failure, preserve the path and error; never search or recreate it.

Use singularity-flow learn. It reads only signed active Capability Pack lessons and materializes inert, secret-scanned UTF-8 files in private Git-common tutorial storage. It never executes them or changes the application checkout, Git history, Devices, a governed Process, approval, certification, or employee score.

1. Resolve exactly one repository from the opened Git root or singularity-flow workspace current --json. Use its returned repositoryPath as cwd. Do not require or attach a Story.
2. Select one requested operation. Never infer a role, Pack, lesson, mission digest, step, check, trust, module, fixture, answer, transfer, or output path. Ask for each missing value.
3. For discovery, use only:
   • singularity-flow learn list --role <ROLE> [--pack <PACK-ID>] --trust <REPOSITORY-FILE> --json
   • singularity-flow learn show <LESSON-ID> --role <ROLE> [--pack <PACK-ID>] --trust <REPOSITORY-FILE> --json
   • singularity-flow learn start|inspect <LESSON-ID> --role <ROLE> --module <REPOSITORY-FILE> [--pack <PACK-ID>] --trust <REPOSITORY-FILE> --json
   • singularity-flow learn explain-change <LESSON-ID> <STEP-ID> --role <ROLE> --module <REPOSITORY-FILE> [--pack <PACK-ID>] --trust <REPOSITORY-FILE> --json
   • singularity-flow learn workspace|progress <MISSION-SHA256> --json
4. materialize, bundle-materialize, progress-import, and reset are machine-local mutations. Run the corresponding command without --confirm once, show its exact effects and confirmation digest, and stop for explicit confirmation. On confirmation, rerun that same operation once with the exact returned digest. Never hand-edit a fixture, module, bundle, manifest, or progress file.
5. A learning check requires an explicit lesson, check ID, role, module, answer file, trust file, and optional Pack. Run singularity-flow learn check ... --json once. Do not display, retain, or summarize answer contents; report only the deterministic result and identity-free progress.
6. Preserve every unavailable, stale-Pack, revoked-Pack, binding, secret, size, tamper, interruption, and recovery diagnostic. Never fall back to unsigned lessons, the live repository, a model, external search, or executable commands embedded in lesson prose.
7. Report the lesson/mission identity, Pack digest, disposable workspace status, completed check IDs, and the explicit boundary: learning only; no authority or certification.

Never run application tests or commands on behalf of a lesson, never copy a fixture into the live checkout, and never treat quiz or teach-back output as approval, performance evaluation, or proof.