nightshift/docs/ideas.md

Ideas TODO

This file tracks open ideas only. Completed items should be removed after they land.

Priority scale:

• P0: do next; directly improves current feedback loop
• P1: important after the current loop is usable
• P2: useful, but only after basics are stable
• P3: defer or maybe reject

P1: Add Test Governance For Generated Tests

Use this only for generated-test mode. Do not put generated tests back into the default DeadDrop fixed-test pipeline yet.

The previous failures proved test-writing agents will:

• edit app code
• import nonexistent modules
• require undeclared dependencies
• inspect implementation internals
• write tests for future behavior

Governance should be deterministic first, model-reviewed second.

Deterministic checks:

• test-writing stage may only touch tests/
• tests compile
• tests import only allowed public interfaces
• tests do not import undeclared dependencies
• tests do not define Flask routes or app implementation
• test names match current task id or current artifact
• no future-task keywords unless accepted by current task acceptance criteria

Then optional model reviewer checks acceptance-criteria alignment.

P0: Preserve Good Drafts During Repair

When a generated file block contains useful allowed content plus disallowed or invalid extra content, avoid redrafting from scratch.

Possible behavior:

• keep the allowed candidate file artifact
• strip disallowed file blocks only when configured as safe for that stage
• continue with validation for the allowed content
• or ask the model for a minimal correction that preserves the accepted candidate

For writing workflows, preserving a good scene is more valuable than forcing a full retry.

P0: Remove Runtime Overrides For Custom Ollama Models

If a model is a tuned local Ollama model such as nightshift-writer or nightshift-base, prefer the Modelfile parameters unless the stage has a specific reason to override them.

Candidate config cleanup:

• remove temperature
• remove num_ctx
• remove num_predict
• remove stop if present

This avoids NightShift accidentally overriding tuned custom-model behavior.

P1: Improve what-happened For Model Runs

The report should identify usable intermediate work, not only final failure state.

Examples:

• model produced a valid scene candidate
• validation rejected extra state files
• recover candidate from candidate-files/<stage>/index.md
• retry output was invalid or too short
• next recommended action

This should make failed creative-writing runs reviewable without manually reading every artifact.

P1: Add Stage-Specific Task Views

The same task may say both "write scene" and "update state", but those responsibilities belong to different stages.

Stage prompts should receive a filtered task view:

• drafter sees only scene-writing criteria
• state updater sees only durable state update criteria
• reviewers see criteria relevant to their review role

This reduces prompt contradiction and makes deterministic stage rules easier for models to follow.

P1: Preserve Intra-Attempt Rerun Artifacts

When NightShift re-runs an agent inside the same stage attempt, do not overwrite the previous artifact.

Examples:

• draft_scene-agent-output.md
• draft_scene-agent-output-invalid-rerun-1.md
• draft_scene-agent-output-1.md

This keeps the initial useful output visible even when strict rerun output is worse.

P1: Classify Writing Review Failures For Repair Routing

The tutorial novel now has a short-term editor stage for review failures, but review failures should eventually be classified before routing.

Candidate classes:

• local_edit: pronoun drift, small continuity issue, missing beat, light style correction
• redraft: wrong premise, broken scene structure, impossible chronology, severe acceptance mismatch
• escalate: ambiguous canon conflict or user preference needed

Route local_edit to the editor, redraft to the drafter, and escalate to a clear user-facing failure. Keep original draft and edited draft artifacts side by side for comparison.

P1: Separate Expressive Drafting From Corrective Editing

If writing quality feels lower after adding stricter correctness constraints, test a two-pass creative workflow:

free_draft -> corrective_edit -> review

The drafter would receive fewer mechanical constraints and focus on voice, scene energy, imagery, and character presence. A later editor pass would enforce:

• output format
• canonical pronouns
• continuity details
• acceptance criteria
• minor cleanup

This may recover stronger prose while keeping correctness guarantees. Treat it as an experiment, not a default, because weaker local models may use the looser draft prompt as permission to ignore task boundaries.

P1: Add A Writing-Mode Validator

Add deterministic checks for prose workflows:

• scene file exists at requested path
• scene word count is within configured range
• drafter did not touch state files
• state updater did not touch chapter prose
• no TODOs, author notes, or bracket placeholders
• optional checks for repeated headings or accidental prompt leakage

This should run before model review stages.

P1: Use Structured State Events For Writing Workflows

Replace model-written full state-file rewrites with compact structured state events, then let NightShift deterministically merge them into durable files such as:

• story/plot-state.md
• story/characters.md
• story/timeline.md
• story/unresolved-threads.md

Candidate state updater output:

events:
  - file: story/plot-state.md
    section: Completed Scenes
    add:
      - SCENE-001 complete; Saint and Miette introduced.
  - file: story/unresolved-threads.md
    section: Open Threads
    add:
      - Saint depends emotionally on Miette and needs compute tokens to keep her present.

NightShift would validate allowed files/sections, reject unknown targets, and apply append/update operations deterministically. This avoids asking a writing model to rewrite entire durable state files after every scene.

P2: Add A Test Analyzer Agent For TDD

Defer until generated tests are stable.

Possible pipeline:

write_tests -> validate_tests -> analyze_tests -> implement

Analyzer output should be concrete:

Implementation requirements:
- create_app(database_path) must return a Flask app.
- POST /snippets must return 201 and JSON id.
- GET /snippets/<id> must return persisted fields.

Do not modify:
- tests/test_task001.py

This may help smaller models, but it is another model output that can be wrong. Add it only after the fixed-test pipeline works through all DeadDrop tasks.

P2/P3: Add A Test Planner

Maybe, but defer.

This overlaps with:

• planner
• test analyzer
• test governance

Too many planning-ish stages can make the pipeline bloated and contradictory.

If implemented later, keep it focused:

test_planner -> write_tests -> test_governance -> implement

For now, fold this idea into the future test governance/analyzer work.

P2: Add Run Comparison

Useful once comparing 14B vs 30B:

nightshift compare-runs --latest 5

Show:

• model
• task
• retries
• failure stage
• final reason
• runtime
• token estimate

This should come after integ-test and integ-report.

P2: Add A Separate Multiagent/Fallback DeadDrop Experiment

Keep the default DeadDrop template boring and deterministic:

planner -> semantic_context -> context -> implement -> validate -> test -> review

If fallback is useful, put it in a separate experiment template, for example:

tutorial-deaddrop-multiagent

or:

examples/templates/multiagent-fallback

Reason:

• fallback makes artifacts harder to reason about
• model variability is bad while debugging pipeline behavior
• the default template should remain the reliability harness