decision_logs
The decision_logs skill documents the architectural choice to create a standalone adversarial-review capability in the ATLAS software engineering agent framework. Use this when understanding how ATLAS reconciles duplicate review tooling, clarifies boundaries between static code review and dynamic adversarial testing, or implements the chosen synthesis approach that makes adversarial review a reusable, target-agnostic protocol rather than embedding it only in the one-shot application flow.
git clone --depth 1 https://github.com/syahiidkamil/Software-Engineer-AI-Agent-Atlas /tmp/decision_logs && cp -r /tmp/decision_logs/docs/decision_logs/2026-06-16-adversarial-review- ~/.claude/skills/decision_logs2026-06-16-adversarial-review-skill.md
# Adversarial-review: a standalone skill, canonical and target-agnostic **Date:** 2026-06-16 **Status:** Accepted **Method:** `free-will` deliberation (medium stakes — a new always-in-context skill that overlaps existing review tooling) ## Context ATLAS already contains the *idea* of adversarial review, but only embedded in `/atlas:oneshotapp` Phase 4 (spawn reviewer subagents with opposing mandates + clean contexts; lenses correctness/security/empty-world; reproduce-before-fixing triage). It is unreachable outside that one-shot flow. Separately there is a `/code-review` skill and a `code-review` agent — both are *static, conservative, confidence-scored* reviews of a diff/PR that **explicitly do not run the app** (`code-review.md`: "Do not check build signal or attempt to build... These will run separately"). The fork: how to scope/structure a new `adversarial-review` skill so it is genuinely reusable and does not duplicate or conflict with those neighbors. ## Branches held open 1. **Urge** — copy Phase 4 into a SKILL.md ~verbatim, self-contained. *Sacrifice:* two copies of the same protocol that will drift; no clear line vs `/code-review`. 2. **Contrarian** — don't add a skill; bolt an "adversarial mode" onto `/code-review`. *Sacrifice:* contradicts the explicit ask, and muddies `/code-review`'s deliberately static/diff scope with app-running reproduction. Rejected — but it sharpens the differentiator. 3. **Synthesis (chosen)** — make `adversarial-review` the *single canonical* definition of the protocol in ATLAS: target-agnostic (diff / feature / running app / whole codebase), encoding the invariants below, with an explicit "when to use this vs `/code-review`" boundary. *Sacrifice:* to stay DRY, `oneshotapp` Phase 4 should later point at it rather than restate it (small follow-up edit, deferred so this change's blast radius stays minimal). 4. **Out-of-box** — make it an *agent* not a skill (rejected: the ask is a skill, and a skill can itself fan out subagents); or a pure protocol skill reused by both `/code-review` and oneshotapp. 5. **Null branch** — don't build it; the concept lives in oneshotapp already. Rejected: that is exactly the gap — you cannot adversarially review an arbitrary existing codebase on demand today. 6. **Precedent (retrieved)** — ATLAS's own oneshotapp Phase 4 (presumed-broken mandate, lenses, reproduce-before-fix); the `free-will` "subagent refutation" pattern (clean opposing context); the `code-review` agent's 0–100 confidence triage. Reuse all three. 7. **First principles (derived)** — a review is *adversarial* iff the reviewer is incentivized to find failure (not confirm success) **and** runs without the builder's context (so it can't inherit the builder's blind spots). Invariants: (1) presumed-broken mandate, (2) context isolation, (3) empirical reproduction to separate real from imagined, (4) senior triage + report. Lenses and reviewer count scale to the target. ## Decision Create `.claude/skills/adversarial-review/SKILL.md` as the canonical, on-demand, **empirical** adversarial review. Differentiator vs `/code-review`, stated in the skill: `/code-review` reads a static diff for bugs/cleanups and never runs the app; `adversarial-review` presumes the thing is broken, fans out clean-context reviewers with diverse lenses, and **reproduces findings by running the target** before believing them. Default invocation (description stays in context) so ATLAS can reach for it autonomously after building something. ## Refutation (survived) - *"Duplicates `/code-review`, users won't know which to run"* → explicit boundary section in the skill. - *"Drifts from oneshotapp Phase 4"* → skill is canonical; Phase 4 dedup logged as follow-up. - *"Target-agnostic = vague"* → concrete target modes (diff · feature · running app · whole codebase) with a stakes-scaling dial for reviewer count. ## Follow-up - Wire `/atlas:oneshotapp` Phase 4 to reference this skill instead of restating the protocol (DRY).
Activate ATLAS pattern recognition mode. Identify repeated patterns, extract essential characteristics, and create reusable abstractions from concrete examples.
Adversarially review something just built — presume it is broken and find where. Use after implementing a feature, finishing a build, or before shipping, or whenever ATLAS or Boss wants a hostile second opinion on a diff, a running app, or a whole codebase. Spawns clean-context reviewers with opposing lenses (correctness, security, empty-world) and reproduces every finding by running the target before believing it. Distinct from /code-review, which statically reads a diff and never runs the app.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Interview Boss about the project, then reason from first principles to design the ideal ATLAS operating identity/system-prompt for it — free to drop KISS/YAGNI/DRY/clean-architecture entirely when the project (and the LLM's own distribution) calls for a different mindset
Designs feature architectures by analyzing existing codebase patterns and conventions, then providing comprehensive implementation blueprints with specific files to create/modify, component designs, data flows, and build sequences
Deeply analyzes existing codebase features by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies to inform new development