# Red Queen

> Adversarial evolutionary QA using Digital Red Queen. Code and tests coevolve via deterministic state machine execution.

- Skill: `lprior-repo/red-queen` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add lprior-repo/red-queen`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lprior-repo/red-queen/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: lprior-repo (https://skillmd.com/u/lprior-repo)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/lprior-repo/red-queen

---


# The Red Queen: Deterministic Adversarial Evolution

## Liza Path

The deterministic state machine lives at:

```
~/.claude/skills/red-queen/liza-advanced.nu
```

All commands use this absolute path:

```bash
L="$HOME/.claude/skills/red-queen/liza-advanced.nu"
nu $L init
nu $L task-add drq-session --spec_ref README.md
# etc.
```

**Always set `L` at the start of every session.**

> *"It takes all the running you can do, to keep in the same place."* — The Red Queen

## Design Principle: Deterministic Over AI

The evolutionary loop has two kinds of operations:

| Operation | Who Does It | Why |
|-----------|------------|-----|
| **Generate test commands** | AI (creative) | Requires understanding of product semantics |
| **Execute tests** | Shell (deterministic) | `run-shell-cmd` — exit code is ground truth |
| **Select survivors** | Liza (deterministic) | exit_code != expect_exit → survivor. No AI judgment. |
| **Lock regressions** | Liza (deterministic) | `done_when` append — permanent, computed, no AI |
| **Gate transitions** | Liza (deterministic) | `assert-can-submit`, `assert-can-review`, `assert-can-merge` |
| **Track generations** | Liza `gen-start` (deterministic) | Increments generation counter on blackboard |
| **Record survivor** | Liza `gen-survivor` (deterministic) | Locks regression + updates landscape atomically |
| **Record discard** | Liza `gen-discard` (deterministic) | Updates landscape tests_run without adding regression |
| **Regression check** | Liza `validate` (deterministic) | Runs ALL `done_when` checks, all must pass |
| **Landscape scoring** | Liza `landscape` (deterministic) | Computes fitness, crown status from blackboard fields |
| **Mutation testing** | Liza `mutate` (deterministic) | Runs cargo-mutants, missed mutants → survivors |
| **Spec mining** | Liza `spec-mine` (deterministic) | Extracts testable promises from README, CLI, doctests |
| **Quality gates** | Liza `quality-gate` (deterministic) | FP gates + DRY + coverage checks, failures → survivors |
| **Fowler review** | Liza `fowler-review` (deterministic) | AST analysis + test smells + security, violations → survivors |

**AI generates test commands. Everything else is deterministic code.**

## The Algorithm

```
DRQ_DETERMINISTIC(target_binary, liza_script):

  # PHASE 0: Initialize deterministic state
  nu $Linit
  nu $Ltask-add drq-session --spec_ref README.md

  # AI: Read docs, generate initial done_when checks (the contract)
  for each promise discovered:
    nu $Ltask-add-check drq-session "<verification_cmd>" --expect_exit=0

  # Claim the session
  nu $Lclaim drq-session red-queen

  generation = 0
  landscape = {}  # dimension → {tests_run: 0, survivors: 0}

  loop:
    # INCREMENT: Deterministic
    nu $Lgen-start drq-session

    # EVOLVE: AI generates test commands for this generation
    # This is the ONLY step that uses AI creativity
    challengers = AI_generate_test_commands(landscape, generation)

    # EXECUTE + SELECT: Deterministic — run each, check exit code
    for cmd, dimension in challengers:
      result = shell(cmd)
      if result.exit_code != 0:  # BUG FOUND
        # Lock regression + update landscape atomically (DETERMINISTIC)
        nu $Lgen-survivor drq-session "<dimension>" "<cmd>" --severity <SEVERITY>
        # File bead IMMEDIATELY — not at session end
        br create --title "[Red Queen] <SEVERITY>: <finding>" --type=bug --priority=<N>
      else:
        # No bug — update landscape tests_run only (DETERMINISTIC)
        nu $Lgen-discard drq-session "<dimension>"

    # ADAPT: Deterministic — computed by liza from blackboard fields
    nu $Llandscape drq-session

    # REGRESSION: Deterministic — Liza runs ALL done_when checks
    nu $Lcoder-submit drq-session red-queen
    nu $Lvalidate drq-session

    # LINEAGE REPLAY: Every predecessor must be defeated
    nu $Llineage-replay drq-session

    # CARNAGE: Track kill rate and lethality
    nu $Lcarnage drq-session

    # AUTOMATED WEAPONS: Run deterministic analysis tools
    nu $Lspec-mine drq-session $TARGET_DIR --bin $BIN
    nu $Lquality-gate drq-session $TARGET_DIR
    nu $Lfowler-review drq-session $TARGET_DIR
    nu $Lmutate drq-session $TARGET_DIR  # Rust only

    # ESCALATION: Auto-promote severity for bleeding dimensions
    nu $Lescalate drq-session

    # EQUILIBRIUM: Requires 3 consecutive zero-survivor gens + all dims exhausted
    # Dormant dimensions reawaken every 5 gens — the Queen never rests

  # VERDICT: Computed from blackboard state
  nu $Llandscape drq-session
```

## What Liza Controls (Deterministic)

Every gate, transition, and regression check is enforced by `liza-advanced.nu`:

### State Machine

```
UNCLAIMED → claim → IN_PROGRESS → coder-submit → READY_FOR_REVIEW
  → validate (runs ALL done_when) → approve/reject
  → APPROVED → merge → MERGED
```

Each transition has a **deterministic gate**:

| Gate | What It Checks | Code |
|------|---------------|------|
| `assert-can-submit` | Status is IN_PROGRESS, agent_id set | Lines 132-139 |
| `assert-can-review` | Status is READY_FOR_REVIEW, commit SHA present, validation results exist | Lines 142-156 |
| `assert-can-merge` | Status is APPROVED, review decision is APPROVED | Lines 159-167 |
| `assert-no-test-weakening` | Changed files don't touch tests/ unless explicitly allowed | Lines 170-177 |

No AI judgment in any gate. Pure field checks.

### Validation (The Ratchet)

`cmd-supervisor-validate` (lines 356-414) is the ratchet mechanism:

```
for each done_when check:
  result = run-shell-cmd(check.cmd)
  pass = (result.exit_code == check.expect_exit)
  # DETERMINISTIC: exit code comparison, nothing else

all_ok = ALL checks pass
# If ANY check fails → validation fails → no approve possible
```

This is the core evolutionary mechanism: **every test that ever broke the code becomes a permanent `done_when` entry**. The validation command runs ALL of them. The code must pass ALL of them. There is no AI involved in this check — it's pure exit code comparison.

### Regression Locking

`cmd-regress` (lines 522-555) adds tests to the permanent bank:

```
# Without --force: deterministically verifies the test fails on champion, passes on candidate
champion_test = run-shell-cmd(cmd)  # must FAIL on current code
candidate_test = run-shell-cmd(cmd)  # must PASS after fix
# Only then: append to done_when
```

This prevents false positives from entering the lineage. Deterministic: two shell commands, two exit code checks.

## Execution Protocol

### Phase 0: Probe (AI + Deterministic)

**AI does**: Read README, --help, source code. Discover promises.
**Deterministic does**: Register each promise as a `done_when` check.

```bash
# Initialize state machine
nu $Linit
nu $Ltask-add drq-session --spec_ref README.md

# AI discovers promises, then registers each deterministically:
nu $Ltask-add-check drq-session "factory help" --expect_exit=0
nu $Ltask-add-check drq-session "factory version" --expect_exit=0
nu $Ltask-add-check drq-session "factory new -s test-slug 2>/dev/null; echo \$?" --expect_exit=0
# ... one per promise

# Claim
nu $Lclaim drq-session red-queen
```

### Generation N: Evolve → Execute → Select → Regress

**AI does**: Generate 3-10 test commands based on the landscape.
**Everything else is deterministic.**

```bash
L="$HOME/.claude/skills/red-queen/liza-advanced.nu"
DIM="error-handling"

# Start generation
nu $L gen-start drq-session

# AI generates test commands (the creative part)
# Execute each, let exit code decide survivor vs discard

# Challenger 1
factory bogus 2>/dev/null
if [ $? -eq 0 ]; then
  # BUG: should have failed — lock survivor + file bead
  nu $L gen-survivor drq-session "$DIM" "factory bogus 2>/dev/null; test \$? -ne 0" --severity CRITICAL
  br create --title "[Red Queen] CRITICAL: bogus command exits 0" --type=bug --priority=0
else
  nu $L gen-discard drq-session "$DIM"
fi

# Challenger 2
factory new 2>/dev/null
if [ $? -eq 0 ]; then
  nu $L gen-survivor drq-session "$DIM" "factory new 2>/dev/null; test \$? -ne 0" --severity MAJOR
  br create --title "[Red Queen] MAJOR: new without --slug exits 0" --type=bug --priority=1
else
  nu $L gen-discard drq-session "$DIM"
fi

# Show landscape (fitness computed from blackboard)
nu $L landscape drq-session

# REGRESS: Run full lineage (deterministic)
nu $L coder-submit drq-session red-queen
nu $L validate drq-session
```

### Landscape Scoring (Deterministic)

After each generation, compute fitness scores from raw counts:

```bash
# Landscape is a simple ratio: survivors / tests_run per dimension
# No AI judgment — pure arithmetic

# Example after generation 3:
# error-handling:    5 survivors / 8 tests = 0.625 (high fitness — keep probing)
# setup:             1 survivor  / 6 tests = 0.167 (low fitness — fewer tests next gen)
# edge-cases:        0 survivors / 4 tests = 0.000 (exhausted if 0 for 2 gens)
# state-management:  3 survivors / 3 tests = 1.000 (everything breaks — maximum pressure)
```

Allocation rule (deterministic, escalating):
- Dimensions with fitness > 0.7: allocate 6+ challengers (HEMORRHAGING)
- Dimensions with fitness > 0.5: allocate 5 challengers (HIGH PRESSURE)
- Dimensions with fitness > 0.3: allocate 4 challengers (CONTESTED)
- Dimensions with fitness > 0.1: allocate 3 challengers (PROBING)
- Dimensions with fitness 0: allocate 2 challengers (COOLING — double-tap, never single)
- Dimensions exhausted for 3+ gens: 0 challengers (but reawaken every 5 gens)
- ALL allocations multiplied by escalation factor: 1.0x + 0.5x per 2 generations
- Equilibrium requires 3 consecutive zero-survivor generations (not 2)

### Coevolution (Fix → Regress → Repeat)

When the DRQ loop is fixing code (not just reporting):

```bash
# 1. AI fixes the code (creative)
# 2. Liza validates ALL done_when (deterministic)
nu $Lcoder-submit drq-session red-queen
nu $Lvalidate drq-session

# If validate fails:
#   → The failing check is ALREADY in done_when (it was there before)
#   → The fix introduced a regression
#   → Fix the regression, re-validate
#   → Repeat until validate passes

# If validate passes:
#   → ALL historical tests pass
#   → Proceed to next generation
```

The ratchet is entirely in liza's `done_when` list and `validate` command. No AI decides if something regressed — the exit code does.

## Automated Weapons

Four deterministic analysis commands that run real tools (not AI heuristics) and record results as survivors/discards in the landscape. Each requires an active generation (`gen-start` first) except `spec-mine` which uses `task-add-check` directly.

### `mutate` — Rust Mutation Testing

Runs `cargo-mutants` and records uncaught mutants as gen-survivors.

```bash
nu $L mutate drq-session /path/to/rust/project --file src/lib.rs --function parse --timeout 300
```

- **Requires**: `cargo-mutants` installed, active generation
- **MissedMutant** → gen-survivor (dimension: `mutation`), auto-escalates to CRITICAL for pub API (lib.rs, mod.rs)
- **CaughtMutant** → gen-discard
- done_when: `cd $dir && cargo mutants -f <file> --re <fn>` expect_exit=0
- Gracefully handles missing `cargo-mutants` with a warning

### `spec-mine` — Language-Agnostic Promise Extraction

Mines any project for testable promises. Does NOT require an active generation — uses `task-add-check` directly to lock permanent checks.

```bash
nu $L spec-mine drq-session /path/to/project --bin myapp --readme /path/to/README.md
```

**What it mines:**

| Source | Method | Dimension |
|--------|--------|-----------|
| README.md | Extract fenced bash/shell/sh/console blocks | `spec-readme` |
| CLI --help | Run `$bin --help`, parse subcommands, add `$bin $subcmd --help` checks | `spec-help` |
| Rust doctests | Grep `/// # Examples` → `cargo test --doc` | `spec-doctest` |
| Python doctests | Grep `>>>` → `python -m doctest <file>` | `spec-doctest` |
| Debt markers | Grep TODO/FIXME/HACK/XXX → observation check | `spec-debt` |
| Rust type safety | `cargo clippy -- -D clippy::unwrap_used` | `spec-type-safety` |

- **Language detection**: Cargo.toml=rust, gleam.toml=gleam, pyproject.toml=python, package.json=node
- All checks are permanent ratchet entries

### `quality-gate` — Deterministic Code Quality Checks (from tdd15)

Runs a battery of deterministic quality checks. Each failing check → gen-survivor.

```bash
nu $L quality-gate drq-session /path/to/project
```

**FP Gates (Rust):**

| Gate | Command | Dimension |
|------|---------|-----------|
| No Panic | `cargo clippy -- -D clippy::unwrap_used -D clippy::expect_used -D clippy::panic` | `fp-gate-no-panic` |
| Exhaustive Match | `cargo clippy -- -D clippy::wildcard_enum_match_arm` | `fp-gate-exhaustive` |
| Format | `cargo fmt --check` | `fp-gate-format` |
| Lint | `cargo clippy -- -D warnings` | `fp-gate-lint` |
| Tests | `cargo test` | `fp-gate-tests` |
| Coverage | `cargo tarpaulin --skip-clean --out json` (< 80% → survivor) | `fp-gate-coverage` |

**FP Gates (Gleam):** `gleam format --check`, `gleam build`, `gleam test`

**DRY Check (Rust):** `cargo clippy -- -D clippy::redundant_clone -D clippy::manual_map -D clippy::unnecessary_wraps` → dimension `quality-dry`

**Test Quality:** tokei JSON → test-to-code ratio < 0.5 → survivor (dimension: `quality-test-coverage`)

### `fowler-review` — Martin Fowler Code + Test Quality Review

Tool-based (not grep heuristic) review of source code AND tests. Each violation → gen-survivor.

```bash
nu $L fowler-review drq-session /path/to/project --complexity-threshold 15 --fn-length-threshold 50 --file-length-threshold 250 --nesting-threshold 4 --coverage-threshold 80.0
```

**4a. Structural Analysis** — `rust-code-analysis-cli` (Mozilla tree-sitter):

| Metric | Threshold | Dimension |
|--------|-----------|-----------|
| Cyclomatic complexity | > 15 | `fowler-complexity` |
| Function length (SLOC) | > 50 | `fowler-large-fn` |
| Nesting depth | > 4 | `fowler-deep-nesting` |
| Cognitive complexity | clippy flag | `fowler-cognitive` |

**4b. AST Pattern Matching** — `ast-grep` (tree-sitter queries):

| Pattern | Dimension |
|---------|-----------|
| `.unwrap()` | `fowler-unwrap` |
| `.expect($MSG)` | `fowler-expect` |
| `todo!()` / `unimplemented!()` | `fowler-todo` |

**4c. Clippy Extended:**

| Check | Dimension |
|-------|-----------|
| Dead code / unused imports | `fowler-dead-code` |
| DRY violations | `fowler-dry` |
| Error handling (unwrap/expect) | `fowler-error-handling` |
| Wildcard enum matches | `fowler-exhaustive` |

**4d. Test Code Review:**

| Smell | Method | Dimension |
|-------|--------|-----------|
| No assertions | `rg -c 'assert' <file>` = 0 for test fn | `fowler-test-no-assert` |
| Test-to-code ratio | tokei JSON, < 0.5 → survivor | `fowler-test-ratio` |
| Coverage | `cargo llvm-cov --fail-under-lines $threshold` | `fowler-test-coverage` |
| Happy path only | < 30% of tests cover error paths | `fowler-test-happy-only` |
| Flaky indicators | sleep calls in tests | `fowler-test-flaky` |
| Test isolation | static mut / lazy_static / Mutex in tests | `fowler-test-isolation` |

**4e. Security & Supply Chain:**

| Check | Tool | Dimension |
|-------|------|-----------|
| Unsafe code | `cargo-geiger` | `fowler-unsafe` |
| Security vulns | `cargo-audit` | `fowler-security` |
| Unused deps | `cargo-udeps` | `fowler-unused-deps` |
| License issues | `cargo-deny` | `fowler-licenses` |

**4f. File Size & Documentation:**

| Check | Method | Dimension |
|-------|--------|-----------|
| File > 250 lines | tokei per-file | `fowler-file-size` |
| Comment ratio < 5% | tokei totals | `fowler-documentation` |

**Required tools (Rust):** `rust-code-analysis-cli`, `ast-grep`/`sg`, `cargo-llvm-cov`, `cargo-geiger`, `cargo-audit`, `cargo-udeps`, `cargo-deny`, `tokei`, `rg`. Missing tools are skipped gracefully.

## What AI Does vs What Code Does

| Step | AI | Deterministic Code |
|------|----|--------------------|
| Discover promises | Reads docs, generates check commands | `task-add-check` stores them |
| Generate challengers | Creates test commands per dimension | — |
| Execute challengers | — | `run-shell-cmd` captures exit code |
| Classify survivor | — | `exit_code != expect_exit` → survivor |
| Lock regression | — | `task-add-check` appends to `done_when` |
| File bead | — | `br create` at same moment as `task-add-check` — never deferred |
| Score landscape | — | `survivors / tests_run` per dimension |
| Allocate next gen | Uses landscape scores to decide dimensions | Scores are computed, allocation follows rules |
| Gate transitions | — | `assert-can-*` functions |
| Validate full lineage | — | `validate` runs all `done_when` checks |
| Fix code | Writes code changes | — |
| Detect regression | — | `validate` fails → regression exists |
| Mine specs | — | `spec-mine` extracts promises from README, CLI, doctests |
| Quality gates | — | `quality-gate` runs FP/DRY/coverage checks |
| Code review | — | `fowler-review` runs AST analysis + test smells + security |
| Mutation testing | — | `mutate` runs cargo-mutants, missed → survivors |
| Track state | — | Blackboard YAML (atomic save) |

**AI touches**: test command generation, code fixes, promise discovery.
**Code handles**: selection, regression, gates, state, validation, scoring.

## Verdict Format

The verdict is **computed from blackboard state**, not AI narrative:

```bash
# Extract deterministic facts from blackboard
nu $Lshow --task=drq-session

# The verdict fields are all computable:
# - generations: count of generation loops executed
# - lineage_size: length of done_when list
# - survivors_by_dimension: group done_when entries by dimension tag
# - crown_status: if ALL done_when pass → DEFENDED
#                 if CRITICAL survivors exist → FORFEIT
#                 else → CONTESTED
```

```
THE RED QUEEN'S VERDICT
═══════════════════════════════════════════════════════════════

Champion:    [product name]
Generations: [N]
Lineage:     [M] survivors (done_when entries)
Final:       CROWN DEFENDED | CROWN CONTESTED | CROWN FORFEIT

FITNESS LANDSCAPE (computed from test results)
═══════════════════════════════════════════════════════════════

Dimension              Tests  Survivors  Fitness  Status
─────────────────────  ─────  ─────────  ───────  ──────────
[computed per dimension from raw counts]

PERMANENT LINEAGE (done_when entries)
═══════════════════════════════════════════════════════════════

[Each entry from done_when: cmd, expect_exit, generation added, dimension]

FULL VALIDATION
═══════════════════════════════════════════════════════════════

[Output of: nu $Lvalidate drq-session]
All checks pass: YES/NO
Failed checks: [list]
```

## Finding Report

Each finding maps to a `done_when` entry — the deterministic artifact:

```
[GEN-{gen}-{n}] {SEVERITY}: {title}
═══════════════════════════════════════════════
Generation:     {N}
Dimension:      {landscape dimension}
Command:        {exact command — this IS the done_when entry}
Expected Exit:  {expect_exit}
Actual Exit:    {what was observed}
Stdout:         {captured}
Stderr:         {captured}

done_when entry: { cmd: "<cmd>", expect_exit: <N> }
Locked by:      task-add-check (deterministic, permanent)
═══════════════════════════════════════════════
```

## Multi-Agent Orchestration

```
RED QUEEN (Orchestrator)
│
├─ SCOUT AGENT (Task: Explore)
│   └─ Phase 0: Read docs, generate initial done_when commands
│   └─ OUTPUT: List of "nu $Ltask-add-check" commands
│
├─ GENERATION AGENTS (Task: general-purpose)
│   └─ INPUT: Landscape scores, lineage (done_when list), generation N
│   └─ AI DOES: Generate test commands
│   └─ DETERMINISTIC: Execute, check exit codes, call task-add-check for survivors
│   └─ OUTPUT: Survivor list, updated landscape scores
│
├─ SOURCE AUDITOR (Task: code-reviewer)
│   └─ Identifies code patterns that inform landscape dimensions
│   └─ OUTPUT: Suggested dimensions to add to landscape
│
└─ ORCHESTRATOR computes:
    └─ Landscape fitness (arithmetic)
    └─ Equilibrium check (2 consecutive zero-survivor gens)
    └─ Crown status (validate pass/fail)
    └─ Verdict (from blackboard state)
```

## Rules of Engagement

1. **Deterministic over AI** — if it can be computed, compute it. AI generates test commands only.
2. **Exit codes are ground truth** — not AI interpretation of output
3. **done_when is the lineage** — every survivor is a permanent shell command with an expected exit code
4. **validate is the ratchet** — runs ALL done_when, deterministic pass/fail
5. **Gates are code** — `assert-can-*` functions, not AI judgment
6. **Landscape is arithmetic** — survivors / tests_run, not AI scoring
7. **State is YAML** — blackboard.yml, atomic save, auditable
8. **AI creativity is bounded** — generate test commands, fix code, read docs. Nothing else.
9. **No AI decides if something passes** — the shell exit code decides
10. **File beads at selection, not at session end** — `br create` happens the same moment as `task-add-check`. Every survivor gets a bead immediately. Never defer.
11. **The Queen always returns** — today's done_when is tomorrow's regression gate
12. **Escalating pressure** — challenger counts increase with generation number (1.0x + 0.5x per 2 gens). The codebase faces ever-growing armies.
13. **Defeat ALL predecessors** — use `lineage-replay` to verify current code defeats every warrior from every generation. New code must beat the entire lineage.
14. **Anti-stagnation** — dormant dimensions reawaken every 5 generations. The Queen never truly rests.
15. **Severity escalation** — 3+ consecutive survivors in a dimension auto-promote severity. Persistent wounds become critical.
16. **Double-tap cooling dimensions** — never send just 1 challenger to a cooling dimension. Always 2+. Confirm the kill.
17. **Carnage tracking** — monitor kill rate and lethality per dimension. The codebase must constantly defend itself.

## Severity Classification

| Severity | Deterministic Criteria | Landscape Effect |
|----------|----------------------|-----------------|
| **CRITICAL** | Core workflow command returns wrong exit code (0 on error, non-0 on success) | dimension.fitness = 1.0 (maximum pressure) |
| **MAJOR** | Documented command fails or produces wrong output (verified by grep/diff, not AI) | dimension.fitness += 0.3 |
| **MINOR** | Output doesn't match documented format (verified by pattern match, not AI) | dimension.fitness += 0.1 |
| **OBSERVATION** | AI-only judgment (no deterministic check possible) | Not added to done_when |

Note: OBSERVATION is the only severity that relies on AI judgment. CRITICAL/MAJOR/MINOR all have deterministic verification commands in done_when.

## Anti-Patterns

| Anti-Pattern | Problem | Deterministic Way |
|--------------|---------|------------------|
| AI decides if test passed | Nondeterministic, unreproducible | Exit code comparison only |
| AI scores the landscape | Subjective, varies between runs | survivors / tests_run ratio |
| AI decides when to stop | No convergence guarantee | Equilibrium: 0 survivors for 2 consecutive gens |
| AI gates transitions | Bypassable, inconsistent | `assert-can-*` functions in liza |
| Tests not persisted | Lost between sessions | `done_when` in blackboard YAML |
| AI judges regression | Flaky, depends on prompt | `validate` runs all checks, exit code comparison |
| Narrative verdict | Different every run | Computed from blackboard fields |

## Quality Gates (All Deterministic)

- [ ] `nu $Lshow --task=drq-session` returns valid state
- [ ] At least 3 generations executed (check generation counter)
- [ ] Every survivor has a `done_when` entry (check done_when length >= survivor count)
- [ ] `nu $Lvalidate drq-session` passes (all done_when checks green)
- [ ] Landscape scores computed (survivors / tests_run for each dimension)
- [ ] Equilibrium checked (0-survivor generations counted)
- [ ] Crown status derived from validate result + survivor severities

## Session Completion

```bash
# 1. Beads already filed (filed at survivor selection, not here)
#    Verify: br list --status=open | grep "Red Queen"

# 2. Verify lineage integrity (deterministic)
nu $Lvalidate drq-session

# 3. Show final state (deterministic)
nu $Lshow --task=drq-session

# 4. Push
git add . && git commit -m "test(red-queen): gen N — <verdict>"
git push
```

---

**Skill Version**: 7.0.0
**Last Updated**: January 2026
**Status**: Production-Ready
**Model**: Deterministic Adversarial Evolution — AI generates tests, code decides outcomes