Security Specialist · L1011 · 2026-07-21

Slice security-specialist/1011 · Level 1011 (Security & Compliance) · Warrior tier · Engine verdict ❌ fail · Walked 2026-07-21

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

🎯 Session Summary

• Character / path: 🛡️ Security Specialist
• Level: 1011 — Security & Compliance, Warrior tier (🔥)
• Quests walked: 5 of 12 (windowed slice, window index 0 of 3; stats.windowed = true)
• Mode: execute — engine evidence pre-computed and sealed by the workflow, then read alongside every quest source as a learner would.
• Headline verdict: ❌ FAIL — avg score 68.8%, split 2 pass · 1 warn · 2 fail.

Quest 1 (security-fundamentals) is a genuinely good conceptual security quest and passes cleanly. The other four are all GH-600 “Agentic Codex” quests — DevOps/AI-ML material that happens to share the 1011 level code but belongs to a different quest line and skill focus than a Security Specialist expects here. Two of them are blocking-broken as written: agentic-multi-agent-orchestration-patterns ships workflow YAML corrupted by leaked Jekyll {​% raw %​} tags inside every ${​{ }​} expression (confirmed by execution) plus three referenced Python scripts that don’t exist, and agentic-behavior-tuning has a baseline script that prints ✅ Baseline recorded while silently failing to write its output file, leaving its own validation unsatisfiable in a fresh environment. A maintainer should treat those two as the priority fixes; the middle survey quest (agentic-codex-05) is the strongest of the chain and actually works.

🗺️ The Journey

🔬 Evidence

All command outcomes below come from the sealed walk-evidence.json execute run (sandboxed, real commands). Items I only judged from reading the source are labelled reasoned.

1. security-fundamentals.md — 81, ✅ pass · ran 3/6 runnable snippets

• docker run --rm -d -p 3000:3000 bkimminich/juice-shop — passed. Image pulled, container started; docker logs showed info: Server listening on port 3000.
• sha256sum app-release.tar.gz — passed. Produced a valid 64-char digest.
• echo "EXPECTED_HASH app-release.tar.gz" | sha256sum --check — passed. Matching hash → OK; deliberately wrong hash → FAILED + WARNING + non-zero exit, exactly as the quest describes.
• macOS brew install --cask docker, Windows winget install Docker.DockerDesktop, Linux sudo apt install docker.io — skipped (Linux sandbox, no sudo/brew/winget). Package IDs verified correct by static review.
• OWASP mapping block, 7-layer defense diagram, GRANT least-privilege example — reasoned. OWASP Top 10:2021 mappings and MySQL GRANT syntax verified correct; no DB provisioned to run the SQL live.

2. agentic-behavior-tuning.md — 55, ❌ fail · ran 2/2 runnable (both failed)

• work/gh-600/scripts/measure_agent_baseline.sh (Exercise 13.1) — failed. Run verbatim in a fresh repo with gh installed but unauthenticated (the realistic first-time-learner state): exited 0, printed ✅ Baseline recorded in work/gh-600/baseline-results.jsonl, but the file was never created (test -f … fails). Root cause: the continue in the no-RUN_ID branch skips the heredoc write for all 3 tasks, yet the final success echo is unconditional.
• Quest Validation self-check — failed. Run verbatim: after the tester manually authored the Ch.3/4 files, 2 of 3 checks printed ✅ (iteration log, changelog); the baseline check stayed silent/❌ because baseline-results.jsonl never existed.
• Mermaid diagram, Ex 13.2 iteration template, Ch.4 CHANGELOG template — reasoned. Valid markdown, but the quest gives no explicit shell command to create the files; the engine had to infer and hand-create them.

3. agentic-codex-05-multi-agent-coordination.md — 87, ✅ pass · ran 7 (all passed)

• Hands-On Lab Steps 1–3 (mkdir, trace(), 8 trace calls, jq -s 'sort_by(.seq)') — passed. Output matched the quest’s documented “Expected” block line-for-line, including the seeded empty handoff at seq 5.
• Ch.2 correlation-ID excerpt (JSONL + $GITHUB_STEP_SUMMARY) — passed. Correct JSONL and ### backend agent — run <cid> summary.
• Ch.2 collect step (cat … | jq -s 'sort_by(.ts)') — passed, with a caveat surfaced: sort_by(.ts) is a string sort; only safe here because date -u +%s.%N yields fixed-width epoch seconds.
• _data/agents.yml, council-fanout.yml, correlation-ID YAML — passed/reasoned (parse cleanly via yaml.safe_load).
• Ch.3 continue-on-error: true + recover if: failure() escalate — reasoned. Not runnable offline; per documented GitHub Actions semantics a job-level continue-on-error: true job reports success to needs-dependents, so if: failure() likely never fires. This is also literally the Mastery Challenge requirement.

4. agentic-multi-agent-orchestration-patterns.md — 45, ❌ fail · ran 6 (3 passed, 3 failed)

• Write orchestrator-fan-out.yml / orchestrator-chain.yml — failed. Files parse as YAML but every ${​{ }​} expression is corrupted into a literal string by interleaved Jekyll tags, e.g. outputs.subtasks == '${​% raw %​}{​{ steps.plan.outputs.subtasks }​}{​% endraw %​}'. I confirmed this directly in the source (line 124 and throughout Ch.2/Ch.3): GitHub Actions only evaluates ${​{ … }​}, so these expressions never fire. Copy-pasting the quest breaks outputs, matrix.subtask, and every needs.*.outputs.*.
• python3 work/gh-600/scripts/partition_task.py … — failed: No such file or directory. partition_task.py, run_subtask.py, aggregate_results.py are referenced by the workflows but never provided anywhere in the quest.
• mkdir -p …, write sub-agent-contract.json, Quest Validation self-check — passed. The JSON schema is valid draft-07. But the self-check prints all three ✅ purely on file-presence, so a learner sees full green on a non-functional pipeline.

5. agentic-multi-agent-observability.md — 76, ⚠️ warn · ran 5 (all passed), 1 skipped

• CORRELATION_ID=task-42-999 python3 …/trace_writer.py — passed. Correct [TRACE] lines + valid JSONL.
• aggregate_traces.py --traces-dir traces … — passed. Filtered by correlation_id, sorted by timestamp, computed agents_involved, printed the failure-detection block for the induced failure.
• Three Ch.4 jq queries (agent filter, status=="failed", human-readable split) — passed, correct output.
• .github/workflows/orchestrator-with-tracing.yml — reasoned. Valid YAML, but it invokes work/gh-600/scripts/traced_subtask.py, which is never defined in the quest — the workflow can’t succeed end-to-end.
• python3 scripts/validate_quest.py --quest q15 (Quest Validation) — skipped. Script does not exist in sandbox or quest content; it’s platform tooling the learner can’t run from the quest alone.

🐞 Issues Found

High

• high · agentic-multi-agent-orchestration-patterns · Ch.2 & Ch.3 YAML blocks (from ~line 124) · tested — Leaked {​% raw %​}/{​% endraw %​} tags are interleaved inside each expression, producing ${​% raw %​}{​{ … }​}{​% endraw %​}, which GitHub Actions will never evaluate. Confirmed via yaml.safe_load (deserializes to the broken literal string) and by direct source read. Fix: wrap the whole fenced block in {​% raw %​}…{​% endraw %​} at the Liquid level (as agentic-codex-05 correctly does at its lines 137/174), leaving the ${​{ … }​} expressions intact.
• high · agentic-multi-agent-orchestration-patterns · fan-out workflow deps · tested — partition_task.py, run_subtask.py, aggregate_results.py are invoked but never supplied (No such file or directory). Fix: ship minimal working stubs, or explicitly mark them as learner-authored placeholders.
• high · agentic-behavior-tuning · Exercise 13.1 measure_agent_baseline.sh · tested — Prints ✅ Baseline recorded while the continue branch skips the only write, so the output file is never created; the success echo is unconditional. Fix: count successfully-recorded tasks and only print success when count > 0, else print a clear skip/failure summary and exit non-zero.
• high · agentic-behavior-tuning · Exercise 13.1 prerequisites + Quest Validation · tested — The baseline (and thus the first validation check) is unattainable without a live authenticated repo containing agent-task.yml and prior agent runs — infra the quest never bootstraps. Fix: provide a minimal bootstrap or label the exercise “requires an existing production-like repo, not completable in a fresh sandbox.”
• high · agentic-multi-agent-observability · Ch.1 workflow traced_subtask.py · tested — The orchestrator workflow calls a script that is never defined in the quest. Fix: include its source, or reuse the already-defined trace_writer.py so the example is self-contained.

Medium

• medium · agentic-codex-05 · Ch.3 escalate pattern + Mastery Challenge · reasoned — continue-on-error: true at job level reports success to needs-dependents, so the recover job’s if: failure() likely never triggers. Fix: use if: needs.agent_security.result == 'failure' (verify on a live run), since this pattern is also literally required by the Mastery Challenge.
• medium · security-fundamentals · objectives vs body (Risk Management) · reasoned — “Risk Management (likelihood × impact)” is a named secondary objective and mastery indicator (“Justify a fix-it-first decision using a simple risk rating”), but no chapter teaches or demonstrates a rating framework. Fix: add a short worked example (e.g., a 3×3 likelihood/impact matrix).
• medium · security-fundamentals · safety warning placement (line 207) · reasoned — The “⚠️ Only ever run intentionally vulnerable apps on isolated/local/throwaway environments” warning lives only inside the collapsible Cloud Realms <details>; the macOS/Windows/Linux readers (the common cases) never see it. Fix: hoist the warning above all four platform tabs.
• medium · agentic-multi-agent-orchestration-patterns · objectives vs content · tested/reasoned — “Event-driven” and “Test aggregation” are stated objectives but never given an exercise. Fix: add worked exercises or trim the objectives.
• medium · agentic-multi-agent-observability · pipeline never wired end-to-end · tested — The three pieces (emit / aggregate / query) are never connected into one runnable workflow; no job downloads sub-agent artifacts and calls aggregate_traces.py. Fix: add the aggregation job (actions/download-artifact@v4 + invoke the aggregator).

Low

• low · security-fundamentals · knowledge checks · reasoned — No answer key anywhere for self-directed learners. Add a collapsed <details> answer block.
• low · security-fundamentals · Advanced Challenge “Break the Juice Shop” · reasoned — No scaffolding/pointer to where the flaws live. Link the official Juice Shop challenge list.
• low · agentic-codex-05 · _data/agents.yml review_date: 2026-07-15 · reasoned — Already in the past relative to today (2026-07-21); either intentional (add a “deliberately overdue” comment) or refresh.
• low · agentic-codex-05 · jq sort_by(.ts) string-sort fragility · tested — Safe only because epoch seconds are fixed-width. Add a | tonumber cast or a footnote.
• low · agentic-multi-agent-observability · Python 3.10+ requirement · reasoned — dict[str, Any] | None needs 3.10+ but no minimum version is stated. Note it.
• low · slice-wide · level hub name mismatch · reasoned — security-fundamentals links [[Level 1011 - Security & Compliance]] while all four agentic quests link [[Level 1011 - Feature Development]] — two names for the same level code (see continuity note below).

🔗 Chain Continuity

This “slice” is two unrelated tracks glued by a shared level code, not one learning path. Quest 1 (security-fundamentals) is authentic Security & Compliance / Warrior content (quest_series: Security Mastery, skill_focus: security, unlocking secure-coding / threat-modeling / pen-testing / compliance). Quests 2–5 are all GH-600 “Agentic Codex” quests (quest_line: gh-600, skill_focus: devops/ai-ml) about tuning and orchestrating GitHub Copilot agents. For a Security Specialist walking level 1011, quest 1 is on-theme and quests 2–5 read as a different curriculum. The planner grouped strictly by level: '1011'; a maintainer may want the level’s theme label and its member quests reconciled. The level-hub naming split (“Security & Compliance” vs “Feature Development” for the same 1011) is the concrete symptom.

Within the agentic sub-chain, the dependency ordering is coherent but the slice is missing its root. The declared chain is Q12 (1010/agentic-failure-root-cause-analysis) → Q13 agentic-behavior-tuning → Q14 agentic-multi-agent-orchestration-patterns → Q15 agentic-multi-agent-observability → Q16. The plan order (behavior-tuning → coordination survey → orchestration-patterns → observability) is a sensible dependency sort. But Q13 hard-requires Q12, which sits at level 1010 outside this window — a learner entering this window fresh has no RCA output, which the engine confirmed leaves Ch.2’s “select top-2 failure patterns” disconnected from real data. Every agentic quest also assumes an authenticated GitHub repo with Actions + Copilot coding-agent access — heavy prerequisites a learner arriving from the conceptual security quest would not have provisioned.

agentic-codex-05 (score 87) overlaps and out-performs its own sub-quests. It is explicitly a Domain 5 survey hub that funnels to four sub-quests including orchestration-patterns (Q14) and observability (Q15). Yet its hands-on lab teaches the same fan-out + correlation-ID + trace-stitch material and actually runs correctly, while Q14 ships copy-paste-broken YAML and Q15 leaves the pipeline unwired. A learner who does the survey gets a working version of what the two dedicated deep-dive quests botch — a strong signal that the fixes to Q14/Q15 can borrow directly from the coordination quest’s verified snippets.

Net continuity verdict: the security-fundamentals entry stands alone and holds; the agentic chain is internally well-ordered but (a) missing its Q12 root in this window, (b) blocked by two learner-stopping bugs mid-chain (Q13 baseline, Q14 workflow), and (c) thematically mismatched to the Security Specialist path. A beginner would clear quest 1, then hit a hard wall at quest 2 (cheerful-but-empty baseline) and quest 4 (green self-check on a broken workflow).

🧠 Reasoning & Method

• What I ran vs. reasoned about: I did not execute the agentic engine — per the workflow contract the evidence was pre-computed deterministically and sealed into walk-evidence.json / walk-evidence.md (the engine’s child claude processes can’t authenticate from an agent’s Bash tool). I consumed that evidence as-is — no edits, no regeneration — and independently read all five quest sources in plan order as a learner to produce the Chain Continuity findings and to cross-check the machine evidence against the actual text. Several issues are corroborated by both the sealed execution and my direct source read (the ${​% raw %​} corruption at line 124; the buried safety warning at line 207; the Risk Management objective absent from every chapter).
• Mode & sandbox: execute mode, sandboxed by the engine in a disposable dir (Linux, gh 2.96.0, Python 3.12). Every passed/failed above traces to a real command in that sandbox; everything I judged from text alone is tagged reasoned.
• Coverage & limits: This is a windowed walk — 5 of the level’s 12 quests (window 0 of 3); the remaining 8 belong to later windows in the perfection sweep. Snippet coverage was partial by nature: OS-specific installs (brew/winget/sudo apt) were skipped on a Linux sandbox; GitHub Actions workflow semantics (the continue-on-error escalate pattern) could not be executed offline and are reasoned, not tested; scripts/validate_quest.py (Q15) was skipped as absent platform tooling. Total engine cost ≈ $2.70 across the five quests.
• Confidence: High on the two FAIL quests — their blocking bugs were reproduced by execution and I re-confirmed them in the source. High on quest 1 and quest 3 passing. Medium on the agentic-codex-05 escalate-pattern doubt (reasoned from documented GH Actions semantics; needs a live-workflow check). I made zero content edits and touched only this one report file.