Data Scientist · L1100 · 2026-07-22

Slice data-scientist/1100 · Level 1100 (Data Engineering) · Master tier · Engine verdict ⚠️ warn (avg 66.6%) · Walked 2026-07-22

🔗 Perfection run · 🏠 Perfection dashboard · 📄 Raw report · 🕘 Change history

🎯 Session Summary

I walked a 5-quest window (window 1 of 3; the full Level 1100 slice holds 15 quests) of the Data Scientist path at Level 1100 — Data Engineering (Master ⚡), playing each quest as a learner in a disposable Linux sandbox. The machine evidence was pre-computed and sealed by the workflow’s execute-engine step (--mode execute); I consumed walk-evidence.json as-is and reasoned about the linked journey on top of it.

Headline verdict: ⚠️ warn. The foundational data-engineering quest (Data Warehousing, 80) and the first automation quest (The Editor’s Eye, 84) are strong and their runnable snippets executed correctly. But two of the five quests fail the rubric (The Agent Pantheon 48, The Autonomy Scales 59) and one warns (The Bard Forge 62), all for the same reason: blocks a learner would actually copy-paste-and-run are broken — leaked Jekyll {​% raw %​} templating, references to scripts/validate_quest.py and helper scripts that no step ever creates, and shell/workflow bugs. None of these are conceptual failures; the teaching is sound throughout. They are fixable follow-along defects, and the two fail quests share a root cause worth fixing once across the level.

An important framing note for the maintainer: this window is a level cross-section, not a single learning line — it interleaves three unrelated quest series (Data Engineering Mastery, The Self-Operating Website, agentic-ai gh-600) that happen to share the 1100 code. See §🔗 Chain Continuity.

🗺️ The Journey

Per-dimension engine scores (1–5): quest 1 commands 4 / accuracy 4 / completeness 3 / clarity 4 / structure 5 / safety 5 · quest 2 commands 4 / accuracy 4 / completeness 4 / clarity 5 / structure 5 / safety 4 · quest 3 commands 2 / accuracy 3 / completeness 3 / clarity 4 / structure 4 / safety 5 · quest 4 commands 2 / accuracy 2 / completeness 2 / clarity 3 / structure 3 / safety 4 · quest 5 commands 2 / accuracy 3 / completeness 3 / clarity 3 / structure 4 / safety 5. The pattern is consistent: commands_work is the collapsing dimension on the three flagged quests.

🔬 Evidence

All statuses below are from commands the sealed execute engine actually ran in the sandbox, or are labelled reasoned where it judged statically. Quotes are trimmed from walk-evidence.json.

1. Data Warehousing — 80 (pass) · ran 7/9 runnable, 2 reasoned, 0 failed

• passed — Linux setup: python3 -m venv .venv && … pip install duckdb ran; python3/sqlite3 already present.
• passed — DuckDB smoke test: python -c "import duckdb; …SELECT 42 AS answer" → “printed the expected answer=42 table” (duckdb 1.5.5).
• passed — Star-schema DDL: “All four tables created successfully in SQLite 3.45.1 with sqlite3 warehouse.db < star_schema.sql; .tables confirmed all four exist.”
• passed (with caveat) — revenue-by-city-by-quarter query: “returned the expected 3 rows sorted by revenue descending once I supplied sample INSERTs myself; run verbatim right after the CREATE TABLE block with no data, it returns an empty result set (no error, but no payoff) — the quest never supplies sample data.”
• passed — snowflake normalization (dim_category + ALTER TABLE) and Type-2 SCD close+insert sequence both executed; final SCD table correctly shows the closed Austin row + new current Denver row.
• passed — columnar_demo.py over range(5_000_000) “ran in ~0.37s … produced correct aggregate output over 5,000,000 rows.”
• reasoned — macOS/Homebrew path (no macOS in sandbox) and Windows/winget path (no Windows). The engine flagged the Windows path installs no sqlite3 CLI unlike the macOS/Linux paths — “a real gap since the quest’s SQL snippets presumably need a sqlite3 client.”

2. The Editor’s Eye — 84 (pass) · ran 4, 3 passed / 1 failed / 1 skipped / 1 reasoned

• passed — Chapter 1 diff-collection: “ran it in a real git repo with an origin remote and a feature branch — git diff --name-only origin/main...HEAD” behaved as documented.
• passed — Smuggle guard: “ran against a simulated PR that added scripts/build.js alongside a content edit: guard correctly detected and blocked it (‘Content PR is smuggling infrastructure changes: scripts/build.js’). Ran again against a clean content-only PR: guard passed.”
• passed — SLAIN loop-breaker: git log -1 --format='%an' vs content-review[bot] produced skip=false for a human commit and skip=true for a bot commit — correct.
• failed — 🐉 BROKEN boss example (lines ~196–211): “with: { ref: ${​{ github.head_ref }​} } fails to parse: PyYAML raises ‘ParserError … expected “,” or “}”’”. The engine notes this is a genuine YAML flow-style bug distinct from the intended lesson (the self-retrigger loop) — an unintended second failure mode a copy-pasting learner would hit.
• skipped — scripts/ai/run.sh content-reviewer executed end-to-end but exited 1 on “Not logged in” (expected — no auth in sandbox); logic path validated.
• reasoned — Quest Network mermaid graph: valid graph LR syntax, static review only (no renderer).

3. The Bard Forge — 62 (warn) · ran 3, 1 passed / 2 failed / 1 reasoned

• passed — bard-forge.yml parsed with PyYAML; on/if/permissions: contents: read structure matches documented behavior (static, not triggered).
• failed — scripts/mine_merge.sh "$PR_NUMBER": “happy path succeeds and emits correct merge_metadata.json … However, the documented failure path is broken: when gh returns empty SHAs, the … [diffstat computation crashes before the friendly guard fires].”
• failed — ‘Open proposal PR’ step: “(1) git commit in the freshly cloned target repo fails with ‘Please tell me who you are’ because no git identity is configured …; (2) cp quest.md target/proposed/… fails because that directory is never created.”
• reasoned — quest-network mermaid: valid syntax; actual render attempted via npx @mermaid-js/mermaid-cli but Chromium/Puppeteer couldn’t launch in the sandbox (environment, not diagram).

4. The Agent Pantheon — 48 (fail) · ran 5, 3 passed / 2 failed / 1 reasoned

• passed — _data/agents.yml: “parsed cleanly, 3 agents with correctly typed fields.”
• passed — PROVISIONING.md and DECOMMISSION.md: well-formed markdown, internally consistent with the registry schema.
• failed — .github/workflows/agent-health-monitor.yml: “the artifact name: field contains literal, unstripped Jekyll tags {​% raw %​}{​{ github.run_id }​}{​% endraw %​} instead of a working ${​{ github.run_id }​} expression, and the workflow’s two run: steps invoke work/gh-600/scripts/load_registry.py and agent_health_check.py” — scripts that exist nowhere in the quest.
• failed — python3 scripts/validate_quest.py --quest q17: ran the exact documented command → can't open file '…/scripts/validate_quest.py': [Errno 2] No such file or directory, exit 2. The validation command as printed cannot succeed standalone.
• reasoned — quest-network mermaid: structurally valid, static review.

5. The Autonomy Scales — 59 (fail) · ran 4, 2 passed / 2 failed / 1 reasoned

• passed — Quest-network mermaid (Q17 --> Q18 --> Q19): “Rendered successfully with npx @mermaid-js/mermaid-cli … produced valid SVG output.”
• passed — _data/autonomy-matrix.yml: “parsed cleanly, all 5 task_classifications entries present.”
• failed — .github/workflows/feature-agent-l2.yml: “the embedded shell contains leaked Jekyll tags ${​% raw %​}{​{ github.event.issue.number }​}{​% endraw %​}; running that exact string in bash gives bad substitution” — plus a \n that renders as literal backslash-n in a gh pr create --body.
• failed — python3 scripts/validate_quest.py --quest q18: can't open file '…/scripts/validate_quest.py' — same missing-script defect as quest 4.
• reasoned — review_autonomy_compliance.sh: bash -n passed; full run needs gh auth login + a workflow named agent-task.yml that no prior step creates (it was named feature-agent-l2.yml), so it errored on auth as expected.

🐞 Issues Found

Every issue below is backed by a command the engine ran or an exact line it quoted. These are for a downstream content pass (content-curator / human) — I did not edit any quest.

1. medium · The Editor’s Eye · Boss Fight 🐉 BROKEN example (lines ~196–211) — Observed: with: { ref: ${​{ github.head_ref }​} } fails PyYAML parse (“expected ‘,’ or ‘}’”). This is an unintended second failure mode on top of the intended self-retrigger lesson, so a learner copying it literally hits a confusing YAML error, not the loop. Fix: use block style (with:\n ref: ${​{ github.head_ref }​}) or quote the expression, matching every other block in the quest.
2. high · The Bard Forge · scripts/mine_merge.sh error handling — Observed: with empty SHAs the diffstat computation crashes before the friendly “Refusing to proceed” guard fires. Fix: move the if [[ -z "$HEAD_SHA" || -z "$MERGE_SHA" ]] check to immediately after the two gh pr view calls.
3. high · The Bard Forge · ‘Open proposal PR’ workflow step — Observed: (a) git commit fails “Please tell me who you are” (no git identity set after gh repo clone); (b) cp … target/proposed/ fails — dir never created. Fix: add git config user.email/user.name in the cloned target repo and mkdir -p target/proposed before the cp.
4. high · The Agent Pantheon · Chapter 2 agent-health-monitor.yml — Observed: literal {​% raw %​}{​{ github.run_id }​}{​% endraw %​} leaked into the artifact name:; and two run: steps call work/gh-600/scripts/load_registry.py / agent_health_check.py that exist nowhere. Fix: strip the raw tags (wrap the fence in a site-level {​% raw %​} block) and either ship the two scripts as quest exercises or link where they live.
5. high · The Autonomy Scales · Chapter 3 feature-agent-l2.yml — Observed: ${​% raw %​}{​{ github.event.issue.number }​}{​% endraw %​} → bash bad substitution when run verbatim, plus a \n that renders literally in gh pr create --body. Fix: wrap the whole fence in one site-level {​% raw %​}; use $'…'/heredoc for the newline.
6. high · The Agent Pantheon & The Autonomy Scales · Quest Validation sections — Observed (both quests): python3 scripts/validate_quest.py --quest q17/--quest q18 → No such file or directory, exit 2. The script is never created by any quest step. Fix (shared): either ship/scaffold validate_quest.py or caveat that it is shared platform tooling requiring the full IT-Journey clone. This is one root cause across two quests — fix once.
7. medium · The Autonomy Scales · Chapter 4 review_autonomy_compliance.sh — Observed: passes bash -n but targets --workflow=agent-task.yml, a name never created (Chapter 3 makes feature-agent-l2.yml). Fix: align the workflow name or state the assumed pre-existing production workflow.
8. medium · Data Warehousing · Chapter 2 star-schema snippets — Observed: the “revenue by city by quarter” payoff query returns an empty set run verbatim because no sample data is ever inserted; it only produced 3 rows after the engine added its own INSERTs. Fix: add a small INSERT seed block before the aggregate query so the Novice Challenge validation shows real output.
9. medium · Data Warehousing · Windows Empire Path — Observed: the Windows/winget setup installs Python + DuckDB but no sqlite3 CLI, unlike the macOS/Linux paths, while the quest’s SQL snippets assume a sqlite3 client. Fix: add a sqlite3 acquisition step (or route Windows learners through DuckDB/python -m sqlite3) to the Windows path.

🔗 Chain Continuity

Reading all five in plan order as one learner, the biggest finding is structural, not per-quest:

• This window is a level cross-section, not a single linked path. The five quests belong to three unrelated series: data-warehousing → Data Engineering Mastery (the actual Data Scientist line); self-operating-website-06/10 → The Self-Operating Website; agentic-multi-agent-lifecycle-management/agentic-autonomy-levels-matrix → agentic-ai gh-600. They share only the 1100 level code. A learner walking them top-to-bottom would experience three context switches (SQL/dimensional modeling → GitHub Actions content review → multi-agent governance) with no narrative bridge. This is expected for a rotating windowed sweep of a whole level, but it means “continuity” must be judged within series, not across the window.
• Within the agentic pair, the dependency edge is real and correct. agentic-multi-agent-lifecycle-management (Q17) declares unlocks: agentic-autonomy-levels-matrix (Q18), and Q18 declares Q17 as required — a genuine, correctly-wired edge, and both are in this window in the right order. But both inherit the same two systemic defects (leaked {​% raw %​} templating + missing scripts/validate_quest.py), so a learner who hits the wall on Q17’s validation will hit the identical wall on Q18. Fixing the shared root cause repairs the pair together.
• The Self-Operating chapters are non-adjacent and their prerequisites are outside the window. Chapter 6 (Editor’s Eye) requires Chapter 5 (The Content Forge, level 1010) — “without a draft-producing pipeline, there is nothing to edit”; Chapter 10 (Bard Forge) requires Chapter 9 (The Chronicle, level 1110) whose published retrospective is “the spark this chapter listens for.” Neither prerequisite is in this slice (they live at 1010/1110), so a learner arriving cold at these two chapters lacks the upstream pipeline they assume. Each quest does flag its prior chapter in prose, which softens the gap, but the window cannot stand alone as a runnable journey for these two.
• Data Warehousing is the one quest that stands cleanly on its own and is the natural “home” quest for the Data Scientist at this level — self-contained setup, prerequisites (etl-pipeline-design) declared, all SQL executable. It is the strongest anchor of the window.

Net: within-series continuity is sound where both endpoints are present (the agentic pair); cross-series continuity is absent by construction; and the two Self-Operating chapters assume out-of-window upstream state. No prerequisite contradiction was found — the gaps are “prerequisite lives elsewhere,” which is correct given the windowed sweep.

🧠 Reasoning & Method

• Mode: execute — the workflow pre-computed and sealed walk-evidence.json / walk-evidence.md with the deterministic agentic execute engine (avg 66.6%, 2 pass · 1 warn · 2 fail, ~$3.38, per-quest sessions 172–286 s). Per the skill’s step 2, I consumed that evidence as-is and did not re-run the engine (its child claude processes can’t authenticate from my Bash tool) and did not edit the plan or evidence files.
• What I ran vs. reasoned: The engine ran the sandboxed commands (SQL DDL/queries in SQLite 3.45.1 + DuckDB 1.5.5, git diff/smuggle-guard/loop-breaker shell, YAML parses via PyYAML, mermaid renders via mermaid-cli, the two validate_quest.py invocations). I independently read all five quest sources in plan order and reasoned about the linked journey, prerequisites, and cross-series structure (§Chain Continuity) — that layer is my analysis, grounded in the quoted frontmatter quest_dependencies and the engine’s command outcomes. Every passed/failed in §Evidence traces to a command the engine actually ran; items I judged statically are labelled reasoned.
• Coverage caps & limits: This is window 1 of 3 of a 15-quest level — I walked 5 quests; the other 10 (windows 2–3) are not covered by this session and the ledger accumulates them over subsequent runs. Sandbox limits the engine flagged honestly: no macOS/Windows (setup paths for those OSes reasoned only, not executed), no network to verify winget package IDs, no auth (so run.sh and gh-dependent steps exited on login prompts as expected), and Puppeteer/Chromium couldn’t launch for one mermaid render (Bard Forge). None of these environment limits were reported as quest defects.
• Overall verdict rationale: warn. The window is walkable and conceptually sound throughout, and its foundational data-engineering quest is strong — but 2 of 5 quests fail the rubric floor on runnable-command fidelity and one warns, driven mostly by two shared, mechanical root causes (leaked {​% raw %​} templating + a missing shared validation script). These are high-confidence, reproducible, and fixable without rewrites. I did not upgrade to pass (multiple failing quests) nor down to fail overall (the slice still teaches correctly and half of it is clean).
• Confidence: High on the per-quest defects (each is a concrete command result or a quoted line). High on the structural continuity finding (grounded in declared quest_dependencies). Medium on the OS-specific setup gaps for macOS/Windows, which were reasoned, not executed.

One slice, one report. No quest content was modified; git is handled by the caller.