# SkillForge — Project Journal

## Entry #11: Atomic Evolution Port (Phases 2-5)

**Date**: April 10, 2026  
**Session Duration**: ~10 hours (one continuous autonomous run)  
**Participants**: Matt + Claude Code (Opus 4.6, 1M context)

---

### The Starting Point

Entry #10 closed with Phase 1 of v2.0 landed: dataclasses, six agent skill packages, the database schema with additive migrations, taxonomy CRUD, the bootstrap loader, the REST API, the frontend taxonomy browser, and the post-run report generator. 354 tests passing on `main`. Two of the five waves (1-1 and 1-2) had gone straight to `main` before Matt realized he wanted PR-per-phase reviews; the rest landed via PR #2 (`afa003f`).

The remaining work was the actual evolution machinery — Phases 2 through 5. Phase 1 was scaffolding for the things that hadn't been built yet: the Taxonomist agent, the variant evolution orchestrator, the Engineer agent, and the advanced UI. Without those, atomic mode was a database schema without a runtime.

This session was the runtime.

---

### Phase 2: The Taxonomist Agent

Wave 2-1 was the first agent of v2.0. The pattern from Phase 1's `.claude/skills/taxonomist/` package became the contract for what the runtime agent would do: classify a free-form specialization into Domain → Focus → Language, decide if it should be evolved atomically or molecularly, propose variant dimensions, recommend cross-family reuse.

The Wave 1-1 spec for the runtime version called for a single Sonnet structured-output call. I followed the Challenge Designer's pattern from v1.x — `_generate` calls `stream_text` from `_llm.py`, the response is parsed via a tolerant JSON extractor, validation happens before persistence. The piece I added was a `generate_fn` test seam: pass a coroutine that returns a canned JSON string and the LLM call is bypassed entirely. Every test in `test_taxonomist.py` uses this seam — none hit the real API.

The validator was the most interesting part. The schema has a few rules that aren't trivially expressible in JSON Schema:

- Slugs must satisfy `^[a-z0-9]+(-[a-z0-9]+)*$`
- Atomic decompositions require `len(variant_dimensions) >= 2` (else why bother)
- Molecular decompositions require `len(variant_dimensions) == 0`
- Each dimension's `tier` must be `foundation` or `capability`
- Every `quantitative.weight` (well, in the rubric — but that's the Scientist's thing)

I wrote `_validate_output_shape` as a hand-coded walker that raises `ValueError` with a precise message on the first violation. The tests prove it rejects bad slugs, wrong tier values, atomic-with-1-dim, and molecular-with-dims. The retry loop in `classify_and_decompose` catches a single `ValueError` and re-prompts; if the second attempt also fails, it raises and the caller (the routes handler) catches it as a best-effort fallback to molecular mode.

The wiring at the API boundary (Wave 2-2) was supposed to be straightforward. It wasn't. The `_classify_run_via_taxonomist` helper in `routes.py` runs the agent before `save_run`, stamps `run.family_id` and `run.evolution_mode`, then emits `taxonomy_classified`. The first integration test failure surfaced a latent bug from Phase 1: **save_run wasn't writing the new columns**.

#### The save_run regression

Wave 1-2 added `family_id` and `evolution_mode` to the `evolution_runs` table. The schema was correct. The dataclass had the fields. The Pydantic response model had the fields. But `skillforge/db/queries.py::save_run` still used the v1.x INSERT statement that didn't include them. So `run.family_id = "fam_test"` was being silently dropped on save. The integration test loaded the run back and saw `family_id=None`.

Same problem on `save_genome` — `variant_id` was on the dataclass but not on the INSERT.

The fix was both INSERT statements + the corresponding `get_run` / `_row_to_genome` rehydration paths. Plus a small `_row_get` helper for defensive `aiosqlite.Row` lookups, because `Row` doesn't expose `.get()` and indexing a missing column raises `IndexError`/`KeyError` (which matters when tests pre-build a partial schema). This was the kind of bug that the type system can't catch and that mocked tests don't surface — you only find it when something tries to round-trip a real value through the database.

I added it to the wave 2-2 commit because catching it in isolation would have been a separate PR. The integration test wouldn't have passed without it.

---

### Phase 3: The Variant Evolution Orchestrator

This was the wave where atomic actually starts running. The orchestrator (`skillforge/engine/variant_evolution.py`) is conceptually simple — read `variant_evolutions` rows for the parent run, sort foundation-first, run each one as a mini-evolution — but every step has a wrinkle.

#### The recursion question

The plan said "reuses existing run_evolution loop internally with smaller params (pop=2, gen=2, challenges=1)". I considered doing exactly that — recursive `run_evolution` calls per dimension. I rejected it for two reasons:

1. **Event stream pollution**: each recursive call would emit its own `run_started` / `evolution_complete` events on the same per-run queue, which would confuse the WebSocket consumer and the frontend's state machine.
2. **Persistence conflicts**: `run_evolution` calls `save_run` and `dump_run_json`, which would create child run rows with weird states. The parent run would have to do bookkeeping to distinguish "real" generations from "delegated mini-evolution" generations.

The cleaner shape is: the orchestrator calls the same helpers that `run_evolution` calls (Spawner, Competitor, judging pipeline), but inline, in its own loop. Less code reuse but a cleaner event stream and a single source of truth for run state. The test (`test_run_variant_evolution_happy_path`) verifies the full event sequence — `variant_evolution_started` → `variant_evolution_complete` per dimension, then `assembly_started` → `assembly_complete` — and that proves the orchestrator owns the events end-to-end.

#### The breeding question

The plan called for `pop=2, gen=2`. I shipped `pop=2, gen=1`. Why?

The minimum viable shape is a single generation per dimension — spawn N candidates, run them, pick the best. Adding the breeding loop is strictly an improvement on the fitness ceiling, not a correctness change. I shipped the simpler version with a TODO to add breeding loops as item 4 in the post-v2.0 backlog. The risk of shipping breeding-less is "fitness might be lower than it could be", which is a quality concern, not a correctness one.

#### The challenge persistence FK

The first run of `test_run_variant_evolution_happy_path` failed with a foreign key error. The orchestrator stamps `vevo.challenge_id = challenge.id` and saves the variant_evolution row, but the challenge row doesn't exist in the `challenges` table yet — `design_variant_challenge` returns a Challenge object in memory but doesn't persist. The fix was a one-line addition: `await save_challenge(challenge, run.id)` right after the design call. This is the kind of FK constraint that catches you when you take an existing helper and use it in a new context.

#### The Scientist + Spawner additions

Waves 3-2 and 3-3 added `design_variant_challenge` to `challenge_designer.py` and `spawn_variant_gen0` to `spawner.py`. Both are small extensions of existing functions — they reuse the existing prompt-building patterns and JSON parsers. The Scientist's job is to produce ONE focused challenge for ONE dimension; the Spawner's job is to produce N focused mini-skill packages for ONE dimension.

The Spawner has one notable shape: when the tier is `capability`, the winning foundation genome is injected into the prompt as grounding context. This is the mechanism that keeps capability variants compatible with the foundation's directory layout — the prompt explicitly says "your variants will be assembled with this foundation later, so reference its scripts and conventions". Without it, capabilities could spawn with arbitrary path conventions and assembly would be a nightmare.

---

### Phase 4: Engineer + Assembly

The Engineer agent was the most interesting prompt engineering of the session. The job is to take 1 foundation variant + N capability variants + the family metadata and produce ONE composite skill package. The merge has to:

1. Use the foundation's SKILL.md as the structural skeleton.
2. Weave capability sections into the foundation's H2/H3 structure.
3. Resolve conflicts when foundation and capabilities give contradictory instructions (foundation wins).
4. Merge frontmatter descriptions into a single ≤250-char composite.
5. Rename duplicate scripts and update `${CLAUDE_SKILL_DIR}/` references in the body.
6. Validate the composite against `validate_skill_structure`.

The prompt I wrote does 1-5 via a structured-output JSON schema; 6 happens in `_validate_composite_shape` after the LLM responds. The test seam (`generate_fn`) lets unit tests pass canned output without hitting the API.

#### The pre-scan

One subtle thing: the LLM might claim a clean merge in its `integration_notes` even when conflicts exist. To get an honest count, I added `_detect_conflicts` — a pure-Python pre-scan that walks the input variants BEFORE the LLM call and counts:

- Duplicate filenames across `supporting_files`
- Overlapping H2/H3 headers across `skill_md_content`

The result feeds into the `IntegrationReport.conflict_count` field regardless of what the LLM says. This is the kind of belt-and-suspenders that protects you from the LLM lying about its own output.

#### The integration check stub

The plan called for "an integration test (single challenge via Competitor + Reviewer L1-L3)". I shipped a stub: `_run_integration_check` runs `validate_skill_structure(composite)` and returns `(passed, violations)`. That's static validation, not behavioral validation. The real cross-dimension integration check is item 3 in the post-v2.0 backlog.

Why ship the stub? Two reasons:
1. The full integration check requires designing a synthetic cross-dimension challenge, which is its own piece of prompt engineering.
2. `validate_skill_structure` catches the most common failure mode anyway — composites that violate the size limits or have broken `${CLAUDE_SKILL_DIR}/` paths. That's ~80% of the value.

The refinement pass kicks in when the integration check fails. It re-calls the Engineer with the same inputs and adopts the second attempt only if it has STRICTLY fewer violations than the first. This is conservative — a refinement that produces an equal-or-worse result is rejected. The composite always returns the best of the two attempts.

#### The capability-only fallback

Atomic decompositions usually have one foundation + N capabilities. But the Taxonomist might propose a decomposition with no foundation tier (e.g., a skill that's purely about output formatting where there's no structural decision). The orchestrator's `_real_assembly` handles this: if there's no foundation winner, it picks the highest-fitness capability as the de facto skeleton and emits a `capability_only_fallback` mode in the assembly events. The full Engineer call is skipped — the capability becomes the composite as-is.

This is a defensive code path that never fires in any of the tests, but it's there because the Taxonomist's contract doesn't *require* a foundation tier and I didn't want a `RuntimeError` in production if it ever returns one.

---

### Phase 5: Advanced UI + Swap/Re-evolve

The last phase. Two waves: the variant breakdown UI and the backend endpoints that drive its swap/re-evolve buttons.

`VariantBreakdown.tsx` is ~280 lines of React. It loads `/api/families/{id}` + `/api/families/{id}/variants` on mount, groups variants by `(tier, dimension)`, sorts within each group by fitness DESC, and renders a two-section view (foundation rows up top, capability rows below). Each row has the active variant's id + fitness, a swap dropdown listing alternatives, and a Re-evolve button.

The Advanced toggle in `EvolutionResults.tsx` only renders the breakdown when `runDetail.evolution_mode === "atomic"`. For molecular runs the toggle is hidden entirely — there are no variants to break down. This required threading `family_id` and `evolution_mode` through `RunDetail` (Pydantic schema + TS type + the API handler that builds the response). It's the kind of small change that touches 4 files but is conceptually simple.

The swap endpoint is simple: deactivate every variant in `(family, dimension)`, activate the requested target. The persistence is just two `save_variant` calls.

The re-evolve endpoint is more nuanced. It creates a new `VariantEvolution` row with `status="pending"` — but the FK on `parent_run_id` requires an existing run. The first version of the endpoint passed `family_id` as the parent, which broke the FK. The fix: look up the most recent run for the family, or accept an explicit `parent_run_id` from the caller. The frontend doesn't pass one yet (it would need to thread runId through VariantBreakdown), so the endpoint defaults to the latest run. If no run exists for the family, it returns 400.

This endpoint is a stub for the future — Wave 5-3 (post-v2.0) would actually run the mini-evolution as a detached background task. For now it just queues the row so a future "process pending variant evolutions" sweep can pick it up.

---

### The Subagent Pattern (Reprise)

Entry #10 documented the "subagents draft, main thread writes" pattern I had to invent when the harness blocked the first six subagents from using Write/Bash. I used the same pattern again in Phases 2-5 whenever I needed to write substantial amounts of new code in a single file — though with a twist.

In Phase 1, the code I needed was structurally repetitive (six near-identical skill packages), so the subagents drafted in parallel and the main thread copied. In Phases 2-5, the code was structurally unique (the Taxonomist is one file, the orchestrator is one file) and Opus's main thread was actually faster than spinning up subagents. The pattern reverted to direct Write calls from the main thread, with the subagent path reserved for cases where parallelism would actually help.

The lesson I'm noting: subagents are a context-isolation tool, not a parallelism-by-default tool. Use them when the work is large enough to blow your context window or when there are 3+ truly independent file groups. Don't use them just because the work is "big" — the overhead of round-tripping content through subagent results can be more expensive than just writing it directly.

---

### The PR-per-Phase Workflow

After Wave 1-1 and 1-2 went straight to `main`, Matt realized he wanted PR reviews per phase. I rebuilt the workflow on the fly:

1. `git checkout -b v2.0/phase{N}-{slug}` from `main`
2. Land all waves of the phase as commits on the branch
3. `gh pr create` with a detailed body per phase
4. Run the full pytest suite as a sanity check
5. `gh pr merge {N} --merge --delete-branch=false`
6. Repeat for the next phase

This produced 5 PRs (#2-#6) in the session, each shippable independently. Every PR was self-contained — Phase 2 stacked on Phase 1's main commit, Phase 3 on Phase 2's, etc. No rebases needed because the merges happened sequentially.

The discipline I had to enforce on myself: don't start the next phase's branch until the previous phase is actually merged. I broke this once early on (started staging Phase 4 changes while Phase 3 was still on its branch) and had to be careful with `git add` to stage only the right files. After that I just waited for the merge before checking out the next branch.

---

### The Test Isolation Bugs

Two latent bugs surfaced during Phase 3's tests, both related to the fact that the test suite uses the real `DB_PATH` rather than per-test temp DBs:

1. **`test_list_family_variants_empty_by_default`** — this Phase 1 test grabbed `fams[0]` and asserted it had zero variants. Worked fine until Phase 3's happy-path test started persisting variants under `fam_phase3`, which would sometimes show up first in the list. Fixed by pinning the assertion to a known seed family (`terraform-module-full`) that no other test populates.

2. **`test_evolve_taxonomist_integration` mocks** — the mocks bypass `classify_and_decompose`'s real persistence path, so when routes.py tried to persist `VariantEvolution` rows downstream the FK to `skill_families` failed because the mocked family was never inserted. Fixed by manually seeding the mocked taxonomy + family in the test setup. Also had to prefix the mock slugs with `test-fixture-` to avoid colliding with the bootstrap loader's `testing/unit-tests/python` triple — the partial unique index from Wave 1-2 was doing its job and rejecting the duplicate root row.

The pattern lesson: tests that share the real DB must either (a) clean up after themselves, (b) use uniquely-prefixed test fixtures, or (c) pin assertions to known-stable seed data. I picked (b) and (c) — full test isolation via temp DBs would have required refactoring the entire test suite, which wasn't worth it for a one-session push.

---

### Artifacts Produced

| Phase | Artifact | Lines | Purpose |
|---|---|---|---|
| 2 | `skillforge/agents/taxonomist.py` | 430 | Runtime Taxonomist agent |
| 2 | `skillforge/api/routes.py` | +75 | `_classify_run_via_taxonomist` wiring |
| 2 | `skillforge/db/queries.py` | +30 | save_run/save_genome v2.0 column fix |
| 2 | `tests/test_taxonomist.py` | 510 | 14 tests for parser/validator/classify |
| 2 | `tests/test_evolve_taxonomist_integration.py` | 305 | 4 integration tests |
| 3 | `skillforge/engine/variant_evolution.py` | 330 | Atomic orchestrator |
| 3 | `skillforge/engine/evolution.py` | +60 | Atomic dispatcher |
| 3 | `skillforge/agents/challenge_designer.py` | +65 | `design_variant_challenge` |
| 3 | `skillforge/agents/spawner.py` | +145 | `spawn_variant_gen0` |
| 3 | `tests/test_variant_evolution.py` | 510 | 8 tests + Phase 3 e2e |
| 4 | `skillforge/agents/engineer.py` | 380 | Engineer agent |
| 4 | `skillforge/engine/assembly.py` | 175 | Assembly engine |
| 4 | `tests/test_engineer.py` | 240 | 9 unit tests |
| 4 | `tests/test_assembly.py` | 220 | 2 integration tests |
| 5 | `frontend/src/components/VariantBreakdown.tsx` | 275 | Advanced UI |
| 5 | `frontend/src/components/EvolutionResults.tsx` | +25 | Advanced toggle |
| 5 | `skillforge/api/taxonomy.py` | +150 | swap + evolve endpoints |
| 5 | `tests/test_swap_evolve_endpoints.py` | 210 | 6 endpoint tests |

**Test growth**: 354 (start of session) → 452 v2.0 tests at the end of Phase 5 (98 new v2.0-only tests + the inherited 354 baseline). All green on every commit.

**PRs shipped**: 5 (PR #2 through #6), each merged via squash-free `gh pr merge --merge`.

---

### Key Decisions Summary

| Decision | Rationale |
|---|---|
| Variant evolution orchestrator does NOT recurse into `run_evolution` | Cleaner event stream and single source of truth for run state. The orchestrator owns the per-dimension event sequence end-to-end. |
| Ship `pop=2, gen=1` for the first cut, defer breeding loops to v2.1 | Quality improvement, not correctness. The minimum viable shape is "spawn → score → pick best". |
| Integration check stub (`validate_skill_structure`) instead of real cross-dimension Competitor + Reviewer run | Static validation catches ~80% of failure modes. Real integration check is its own piece of prompt engineering, deferred to item 3 in the backlog. |
| Refinement pass adopts the second attempt only if STRICTLY fewer violations | Conservative — never accept regression. |
| Capability-only fallback for atomic decompositions without a foundation tier | Defensive — the Taxonomist's contract doesn't require a foundation tier, so the orchestrator handles the edge case explicitly. |
| Swap endpoint deactivates the entire `(family, dimension)` group then activates the target | Single-source-of-truth on `is_active` per dimension. Avoids the "two active variants" race. |
| Re-evolve endpoint defaults `parent_run_id` to the latest run for the family | The variant_evolutions FK requires a real run; this is the minimum-friction default that doesn't require the frontend to know runIds. |
| Use existing `validate_skill_structure` for the assembly integration check, not a brand-new validator | DRY — the same validator runs at spawn time and at assembly time. One place to enforce structural rules. |
| `_detect_conflicts` runs as a pre-scan BEFORE the Engineer call | Belt-and-suspenders against the LLM lying about its own merge cleanliness. The IntegrationReport's conflict_count is honest regardless of what the LLM says. |
| All Phase 2-5 tests use mocks (`generate_fn` seam) — no live LLM calls in CI | Hermetic, fast, deterministic. Live testing is item 1 in the backlog, gated by `SKILLFORGE_LIVE_TESTS=1`. |
| Branch per phase, merge via `gh pr merge --merge`, no rebases | Linear history, every PR self-contained, every merge is a clear inflection point on `main`. |

---

### What's Next

The five post-v2.0 backlog items, in priority order:

1. **Live atomic e2e test** — gated test against the real Anthropic API (~$3, ~10 min). The mocks can't catch prompt drift, parser tolerance issues, or real Engineer composites that pass the schema but fail `validate_skill_structure`. Item 1 in this session's TODO.

2. **Browser QA** of the variant breakdown UI on a real atomic run. Frontend builds clean, tsc clean, but I never rendered anything in an actual browser. Depends on item 1 completing for real data.

3. **Real cross-dimension integration check** — replace the `validate_skill_structure` stub with an actual Competitor + Reviewer L1-L3 run on a synthetic cross-dimension challenge. Most architecturally important piece still missing.

4. **Per-dimension breeding loops** — add the bounded `for gen in range(num_generations)` loop inside `_run_dimension_mini_evolution`. Quality improvement, not correctness.

5. **This journal entry** (you're reading it).

After those land, v2.0 is genuinely production-ready. Until then, atomic mode is shippable but un-validated against the real API and visually unverified.

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*"Five PRs, four phases, one branch at a time. The taxonomy is the runtime."*