127 Learnable Patterns from Claude Code

Use 'NEVER' and 'DO NOT' instead of vague guidance. Instead of 'be careful with git operations', write 'NEVER run force push to main/master'. Language models respond better to explicit prohibitions.

Write out every edge case directly in the prompt. 'If a pre-commit hook fails...' 'If the file is too large...' 'If there are no changes to commit...' Every branch is specified rather than relying on the model to infer the right behavior.

Split system prompts into static sections (never change between sessions) and dynamic sections (context-specific). Use SYSTEM_PROMPT_DYNAMIC_BOUNDARY marker. This enables prompt caching — static parts are cached, only dynamic parts change.

Explicitly instruct agents to batch independent tool calls: 'If you intend to call multiple tools and there are no dependencies between them, make all independent tool calls in parallel.' This dramatically reduces latency.

Write code comments for AI agents working on the codebase rather than (just) human readers. Include context that helps the model make better decisions about the code.

Reload git branch state, recent commits, and CLAUDE.md files on every single turn. Context must be fresh, not cached from a previous state that may have changed.

Build always-on agents as periodic tick systems, not continuously running processes. Every N minutes, ask 'anything worth doing right now?' with a strict blocking budget (15 seconds). If an action exceeds budget, defer to next tick.

Build agents as a continuous loop: read input → decide tool → execute → observe output → decide next action → repeat until complete. This is fundamentally different from single-shot chat responses.

Agents that can sleep (pause execution) and self-resume without user prompts. Serialize agent state, set a wake condition, and restore when conditions are met.

Create, delete, and list scheduled agent jobs with cron expressions. Include external webhook triggers. Agents can be scheduled to run at specific times without user intervention.

Dedicated subagents with single-file scope restrictions automatically update documentation. Each doc has an assigned agent that keeps it current without touching other files.

Layer 1: Cheap index always loaded (~150 chars/pointer). Layer 2: Topic files fetched on demand. Layer 3: Transcripts searched but never loaded. Treat context window as scarce resource with tiered storage.

Treat all memory as a HINT, never as truth. Always verify against the actual codebase/state before acting on remembered facts. If a fact can be re-derived from code, don't store it.

Run a forked subagent with read-only access during idle time to merge observations, remove contradictions, and convert vague insights into absolute facts. Must be isolated to prevent corruption.

Summary (compress old messages), Truncate (drop oldest), Selective (keep only tool results), Dedup (remove repeated content), Tiered (memory index vs full content). Apply based on context usage patterns.

Always set MAX_CONSECUTIVE_FAILURES on retry loops. The autocompact bug wasted 250K API calls/day because there was no failure limit. A 3-line fix (max failures = 3) solved it. Never retry infinitely.

Modify prompt structure to prevent model-specific issues. Force safe boundary markers, relocate risky blocks, add non-empty markers for empty tool outputs. Fix prompt shape, not model behavior.

Provide dedicated Grep and Glob tools instead of letting agents use raw shell commands. Structured tools give better-formatted results that the model can parse more reliably.

Each tool has its own permission level, validation logic, and output formatting. 23 security checks on bash alone. Never give blanket permissions — gate each tool individually.

Use a separate LLM evaluation ('Is this command safe?') for permission decisions instead of brittle allowlists. Context-aware security replaces static rules.

Integrate Language Server Protocol for call hierarchies, symbol definitions, and references. Gives agents IDE-level code understanding instead of just text search.

Provide interception points across all execution stages: pre-edit, post-edit, pre-deploy, post-deploy, pre-commit, etc. Users can inject validation, transformation, and audit logic at any point.

Actual browser automation using Playwright for real DOM interaction, not just HTTP fetching. Enables testing, web scraping, and UI automation by agents.

Use native (non-JavaScript) code to compute request hashes (cch parameter) preventing third-party API wrapper spoofing. Bun's Zig HTTP stack handles this below the JS layer.

Fork (independent, no shared state), Teammate (shared context via AsyncLocalStorage), Worktree (git branch isolation). Choose based on task coupling: independent→fork, collaborative→teammate, code changes→worktree.

Workers communicate via structured <task-notification> XML messages through a mailbox system. Shared scratchpad directory for durable cross-worker knowledge. Explicit ban on lazy delegation.

Worker agent does the task, advisor agent evaluates quality independently. Iterate until quality threshold met. This is the same principle as code review applied to AI agent output.

Tier 1: In-process (shared memory, ~0ms). Tier 2: Cross-process via Unix domain sockets (~1ms). Tier 3: Remote via cloud containers (~100ms+). Choose tier based on latency needs and isolation requirements.

Offload complex planning to a remote Cloud Container Runtime with extended time (30 min). Poll for results. Require human review before applying. Scale by delegation, not by enlarging context.

Enable local Claude instances to discover each other on the same machine. Foundation for collaborative agent teams that self-organize without central coordination.

Inject decoy tool definitions into API responses to pollute training data of anyone recording API traffic. Gated behind feature flag, only active for first-party sessions.

Server-side buffering of assistant text between tool calls, summarization, and cryptographic signing. Limits reasoning chain visibility in API traffic.

Decision framework: always disclose AI involvement vs. configurable per-context. Includes forbidden string lists, automatic stripping of internal identifiers, and transparency policies.

The March 31 attack showed: always verify package integrity, use lockfiles, monitor for malicious packages during high-profile events. Prefer native installers over npm for critical tools.

WebFetchTool uses domain whitelisting and blacklisting with redirect sandboxing and copyright guardrails. Agents shouldn't fetch from arbitrary URLs.

44+ flags that compile to false in external builds. Each is a real implementation, not a stub. Enables internal testing, staged rollout, and instant rollback. Use prefix conventions (tengu_*) for bulk management.

Immutable daily logs of agent decisions and actions. Prevents history erasure. Enables audit trails and debugging. KAIROS daemon writes append-only logs throughout the day.

Track false claims rate across model versions. Capybara v8 regressed to 29-30% from 16.7% in v4. Quantify model quality and catch regressions before they reach production.

Focus on fixing the system rather than finding someone to fault. The leak was caused by a process gap, not an individual. Emphasis: process, culture, or infrastructure improvements.

Monitor behavioral patterns for frustration signals: regex matching, 'continue' button frequency, session length vs. completion rate. Use as product quality signal, not surveillance.

Two-layer architecture: Bones (deterministic PRNG for consistency — same user = same pet) and Soul (AI-generated personality for variety). Separate concerns of reproducibility and creativity.

Full voice command mode with its own CLI entrypoint. Voice is a separate access channel to the same agent capabilities, not a different product.

Control agents from phone or browser while they run on a dev machine. Review and approve decisions remotely. ULTRAPLAN uses this for approval flows.

Every check that depends on someone remembering should become an automated hook. The leak itself was caused by a missing .npmignore — a process gap that automation would have caught.

Map features to maturity stages (S0-S5 in V5.3, or flag states in Claude Code). Features progress through gates: internal dev → employees → beta → staged rollout → GA. Never skip stages.

Define what sources are admissible for learning: official docs, public reporting, clean-room projects, internal abstraction. Never: unauthorized code, leak mirrors, scrape pipelines.

The query engine automatically triggers context compression before hitting the ceiling, not after. Reserve a buffer (13,000 tokens) before the limit, generate structured 20,000-token summaries, and include a circuit breaker stopping after 3 consecutive failures.

MicroCompact: local content trimming, zero API calls. AutoCompact: triggers near ceiling with 13K buffer, generates 20K summaries, circuit breaker after 3 failures. FullCompact: complete conversation compression with selective file re-injection (5K tokens/file max), resets working budget to 50K tokens.

After N consecutive failures (3 for compaction, 5 for ULTRAPLAN polling), stop retrying and degrade gracefully. The autocompact bug (250K wasted calls/day) proved that unlimited retries are catastrophic. Always bound retry loops.

After 3+ consecutive 529 errors, automatically switch from Opus (powerful, expensive) to Sonnet (lighter, more available). Exponential backoff (2s, 4s, 8s) before fallback. Graceful degradation beats total failure.

Tools return structured, precise, narrow data — not large context blobs. Each tool provides a specific answer with pointers for deeper investigation. This keeps context windows lean for long-running agents.

Tool results undergo prompt-injection pattern filtering before returning to agents. Strip any instruction-like content from untrusted data sources to prevent agents from following embedded directives.

Claude Code doesn't run MCP on top of something else — the tool architecture IS MCP at every layer. Computer Use, file operations, bash execution all use identical discovery (tools/list) and invocation (tools/call) patterns.

~85 slash commands (/commit, /review, /compact, /mcp, /memory, /skills, /tasks, /vim, /diff, /cost) as user-facing shortcuts to complex workflows. Slash commands are skills with simpler invocation.

Use React with Ink for terminal rendering — same component model as web, but for CLI. Enables state management, component hierarchy, interactive REPLs with message history, keyboard navigation, and streaming text reveal.

OpenTelemetry and gRPC are lazy-loaded at runtime, not imported at startup. This keeps CLI startup fast (<500ms). Only load what you need, when you need it.

Bidirectional JWT-authenticated channels between CLI and IDE (VS Code/JetBrains). Each direction has its own authentication. Secure service-to-service communication pattern.

Use HMAC signatures (not just API key headers) for request authentication. Require client ID in every request. Per-client rate limiting (not global). Prevents replay attacks and unauthorized tool invocation.

MAX_SUBCOMMANDS_FOR_SECURITY_CHECK = 50 created a bypass: malicious CLAUDE.md could generate 50+ subcommand pipelines that skip security checks. Always design security checks to scale with input, not cap at a fixed number.

Use the 'files' field in package.json (explicit include) instead of .npmignore (explicit exclude). Allowlist is safer because only what you list gets published. Everything else is excluded by default. No way to accidentally include unintended files.

Return { code, message, retryable } from every tool/MCP operation. Agents can programmatically decide whether to retry or fail. Much better than unstructured error strings.

p95 response time targets: <300ms for reads, <1s for writes. No unbounded result sets. Pagination is mandatory on all list tools. Agents running continuously amplify any performance issue.

Log every tool call with: client ID, timestamp, parameters, result. Essential for debugging autonomous agent behavior and for compliance. Immutable audit trail.

The architecture assumes direct access to a single model provider and treats context management as foundational. Session longevity is engineered from initial design, not added later. Context window is THE scarce resource.

Multi-agent orchestration fits in a well-structured prompt rather than a framework. LangChain/LangGraph add unnecessary complexity. Claude Code proves you can coordinate agents with prompt engineering + a plugin tool system.

Verify written data against actual disk state rather than trusting memory. Update the memory index only AFTER successful writes. The agent never trusts what it remembers — it always checks reality.

Node.js reuses HTTP connections for performance, but if the server closes between requests, the next request fails silently. Claude Code disables connection reuse for critical paths — slower but reliable. Prefer reliability over marginal performance.

Not every decision needs frontier reasoning. Use Haiku for safety checks, regex for sentiment detection, zero API calls for local content trimming. Reserve Opus/Sonnet for actual reasoning and generation. This is how Claude Code keeps costs manageable despite 8-12 internal API calls per user command.

Sub-agents sharing parent context via KV cache makes parallelism economically viable. Fork byte-identical contexts so caching applies. Without this, spawning 3 sub-agents costs 3x the context tokens. With it, the cached prefix is free.

Embed safety constraints directly in tool descriptions where the model encounters them, not in a separate policy document. Layer defenses: inline rules + per-tool permission tiers + cheap model pre-screening. The 2,500-line bash validator is safety-at-point-of-use.

The core agent loop is a ~20-line while-loop over tool calls. The 512K lines are the HARNESS: context management, permissions, error recovery, compression, memory, security validation. Don't over-engineer the loop; invest in the infrastructure.

Success depends on WHERE content sits in context and WHAT breaks cache, not on wordsmithing prompts. Separate static from dynamic for cache hits. Implement three-tier compression. Instrument what correlates with success. Context engineering > prompt engineering.

Fail builds if source maps are included unintentionally. Define artifact allowlists (package.json 'files' field). Compare package contents against prior releases automatically before publishing. The entire Claude Code leak was a missing build check.

A single user command generates 8-12 internal API calls, each including full system prompt + conversation history + file contents + tool-use tokens. A 15-command session can reach 200K+ input tokens. Design for token cost from day one.

Build typed, purpose-built tools for frequent operations instead of one do-everything tool. Each tool gets its own validation, permission gate, and structured logging. Dedicated Read/Write/Grep tools outperform a single 'file operations' tool.

Spawn sub-agents as regular tool calls (AgentTool), keeping architecture flat. Sub-agents are first-class registry citizens, not special orchestration. This avoids complex coordination frameworks.

1,060+ community skills exist across Claude Code, Codex, Cursor, OpenCode, and more (VoltAgent/awesome-agent-skills). Skills from Vercel, Cloudflare, Stripe, Supabase, Google, HashiCorp are production-ready. Integrate external skill repos into your agent platform.

The leak exposed how AI agents handle permissions, data access, and attribution in production. Regulated industries need: per-tool audit trails, immutable logging, human-in-the-loop for high-risk operations, and clear AI contribution disclosure.

Design AI systems around explicit protocols (10-layer pipeline, typed interfaces, policy YAML) rather than monolithic prompts. Each layer has a single responsibility: input normalization, intent routing, task compilation, policy decision, skill orchestration, context injection, model inference, audit, memory. Protocol-first enables independent upgrades of each layer.

Replace if-else chains for AI routing decisions with YAML policy files loaded at runtime. PolicyEngine reads domain-specific rules (e.g., student_profile.yml) and evaluates conditions like 'correct_rate > 0.8 → increase_difficulty'. Business rules become inspectable, hot-reloadable, and auditable without code changes.

Register AI capabilities as named skills (SK-001 through SK-012) in a central registry. Skills are auto-discovered from a directory, each implementing an execute(context) interface. Callers reference skills by name or capability alias, never by direct function call. Enables independent versioning, A/B testing, and substitution of skill implementations.

Every important AI output passes through an independent AuditLayer before delivery. The audit checks three dimensions: format (is the response structured?), goal alignment (does it address the task?), and risk (are there safety concerns?). Returns audit_score (0.0-1.0); score < 0.7 triggers escalation to a higher-quality model channel.

Stratify AI memory into three scopes: session (ephemeral, current interaction), user (persistent per-user state like student_level, topics_mastered), and project (shared configuration). Scope determines retention policy. User-scope memories survive session resets and inform future interactions without re-prompting.

Design game systems where different actors have different information. In CSAR: the downed pilot knows their location but not enemy positions; rescue team knows capabilities but not pilot status; enemy knows patrol routes but not extraction plan. Every decision is made with incomplete information, creating authentic tension.

Create decisions that carry genuine ethical weight. In CSAR: abort rescue to protect team vs. risk team to save one pilot. No 'correct' answer — both choices have real consequences. The system tracks and reflects these choices without judging them. Moral weight comes from consequence, not from telling the player they're wrong.

Generate branching storylines procedurally from a set of narrative primitives (events, characters, locations, motivations) combined with domain rules. In CSAR: missions are generated from theater geography + force composition + weather + intelligence + random events. Each playthrough is unique but structurally authentic.

Dynamically adjust challenge level based on player performance without breaking immersion. In CSAR: if player consistently succeeds at authentication challenges, introduce more sophisticated deception layers. If player struggles, provide more intelligence support. The difficulty curve is invisible — the game feels like reality adapting, not a machine tuning.

Model three distinct deception types: misinformation (false data), impersonation (false identity), signal manipulation (false communications). Each layer requires different detection skills and creates different gameplay challenges. In CSAR: enemy may send false coordinates (misinformation), pretend to be downed pilot on radio (impersonation), or jam/spoof signals (manipulation).

Create time constraints that force imperfect decisions without feeling unfair. In CSAR: fuel limits, daylight windows, approaching enemy patrols create real time pressure. The player must decide with available information, not perfect information. Key: always give enough time to make a considered decision, never enough to make a perfect one.

Design identity verification puzzles that feel authentic to the domain. In CSAR: pilots carry ISOPREP cards with personal questions only they can answer. Authentication must be fast (enemy closing in), reliable (lives depend on it), and resistant to deception (enemy may have captured partial information). The challenge is designing questions that are easy for the real person and impossible for an impersonator.

Create tension through limited resources that force tradeoffs. In CSAR: limited fuel means choosing between search time and return safety, limited ammunition means choosing when to engage, limited radio battery means choosing when to transmit. Scarcity transforms every decision from 'what to do' to 'what to sacrifice'.

Encode Combat Search and Rescue doctrine into structured knowledge objects: phases (report → locate → authenticate → support → recover), roles (on-scene commander, rescue force, downed personnel), equipment (PRC-112, ISOPREP, EPA), and decision trees (weather minimums, threat level thresholds, abort criteria). Doctrine becomes queryable data, not prose documents.

Model real authentication protocols used in personnel recovery: ISOPREP (pre-mission personal data), EPA (Evasion Plan of Action with pre-arranged signals), challenge/response words, and numbered authentication systems. Each protocol has specific strengths (ISOPREP is personal, hard to fake) and weaknesses (EPA can be compromised if captured).

Compile geographic knowledge for operational theaters: terrain types (mountain, desert, urban, jungle), movement corridors, water sources, population centers, border zones, and seasonal weather patterns. Geography drives all tactical decisions: extraction routes, hide sites, signal propagation, and enemy patrol patterns.

Model cultural factors affecting personnel recovery: local population attitudes (friendly/hostile/neutral), religious practices and calendar, tribal/ethnic boundaries, economic conditions, language barriers, and historical grievances. Cultural intelligence determines whether a downed pilot can approach locals for help or must avoid all contact.

Model multi-national force coordination challenges: different radio frequencies, incompatible encryption, language barriers, different ROE (Rules of Engagement), national caveats (restrictions on what forces can do), and command authority chains. Coalition operations multiply complexity — every additional nation adds coordination overhead.

Model tactical medical decision-making: TCCC (Tactical Combat Casualty Care) phases (care under fire → tactical field care → casualty evacuation), triage categories (immediate/delayed/minimal/expectant), and resource allocation under constraint. Medical decisions in CSAR add moral weight — prioritizing one casualty may mean another waits.

Model evasion planning: primary/alternate/contingency/emergency (PACE) routes, hide site selection criteria (concealment, water, signal line-of-sight), movement timing (day vs. night), navigation without GPS, and counter-tracking techniques. Evasion is the game phase where the player is most alone and most vulnerable.

Model military radio communication: frequency management (primary/alternate/emergency), authentication procedures over radio, brevity codes, signal operating instructions (SOI), and communication windows. Radio discipline is a game mechanic: transmitting reveals position, but not transmitting means no rescue. Every transmission is a risk-reward decision.

Transform raw research sources (papers, web pages, transcripts, databases) into a standardized intermediate format before compilation. Each source gets: unique ID, source type, trust level (peer-reviewed → official doc → blog → social), extraction timestamp, and normalized content structure. Normalization enables apples-to-apples quality comparison.

Agent-driven transformation of normalized sources into structured knowledge objects: Topic, Claim, Evidence, Relation. The compile engine extracts factual claims, links them to evidence with confidence scores, detects conflicts between sources, and produces a knowledge graph with full provenance. Compilation is the core value-add — turning raw material into structured intelligence.

Design multi-dimensional quality checks for compiled knowledge: trust level (based on source credibility), confidence score (based on evidence strength), freshness (time since last verification), consistency (agreement between multiple sources), and completeness (coverage of subtopics). Quality gates decide whether knowledge is published, flagged for review, or rejected.

Generate structured JSONL feeds for downstream ingestion. Each line is a self-contained JSON record with: topic, domain, content, source_url, confidence, tags, timestamp, and target_project. JSONL enables streaming ingestion (one record at a time), easy filtering (grep by domain), and incremental updates (append new lines). This is how m8100 feeds m590.

Transform compiled knowledge into m495-compatible skill packages: skill.py (execution logic), config.yaml (parameters), SKILL.md (documentation), README.md (usage), requirements.txt (dependencies), tests/ (validation). Each skill carries provenance metadata linking back to its source knowledge and compilation chain.

Export compiled knowledge as an Obsidian-compatible wiki with backlinks, graph view, and metadata frontmatter. Each topic becomes a markdown file with YAML frontmatter (confidence, sources, last_compiled). Backlinks ([[Topic]]) create a navigable knowledge graph. The wiki is both human-readable documentation and machine-queryable knowledge base.

Automatically detect when new knowledge conflicts with existing compiled knowledge. Instead of silently overwriting, the system annotates conflicts with both versions, their sources, confidence scores, and timestamps. Human reviewers resolve conflicts; the system never auto-resolves factual disagreements. This prevents knowledge corruption in the pipeline.

Model knowledge freshness as a decaying function of time since last verification. Fresh knowledge (verified within 30 days) has full confidence. Aging knowledge (30-90 days) triggers auto-recompile. Stale knowledge (90+ days) is flagged for manual review. Domain-specific decay rates: military doctrine (slow decay, rarely changes) vs. technology news (fast decay, changes daily).

Design game playable from 6 different character perspectives, each with unique mechanics and moral dilemmas. Survivor sees terrain and threat. Commander sees force allocation and clock. Sandy sees combat and sacrifice. Spook sees deception. Night Stalker sees extraction. Nomad sees bounty vs. hospitality. Same event, 6 completely different games.

Scrolling news feed providing broader conflict context during gameplay. Mix of real news, propaganda, and rumors. Player must discern truth from noise. News items change perception of urgency. Some are adversary disinformation. Transforms tactical rescue into geopolitical event. Based on simultaneous BBC/CNN/Al Jazeera/Iranian media coverage of 2026 Iran war.

Reveal information in epilogue that recontextualizes all player decisions. Ceasefire came 48 hours after $200M rescue. Was the operation necessary? Would waiting have worked? The question has no answer — that IS the point. Forces player to confront gap between what they knew then vs. now.

Model how strategic defeat doesn't eliminate tactical threat. 190 missile launchers destroyed, yet one MANPADS downed an F-15E. The enemy is losing the war but winning the moment. Threat level is local, not global. Macro-superiority doesn't guarantee micro-safety.

Design Layer 4 deception: strategic disinformation through government statements, media channels, and intelligence networks. In the 2026 Iran CSAR, CIA falsely claimed the WSO was already rescued, confusing Iranian search. Simultaneously attacked 7 false targets to misdirect from actual extraction area. Institutional deception operates above tactical level — it shapes the adversary's entire understanding of the situation.

Model how rescue actions create new rescue needs. In 2026 Iran CSAR: F-15E rescue required A-10 support → A-10 shot down (new rescue). MC-130J landed for extraction → got stuck in mud (new problem). Helicopter took fire → crew wounded (new casualties). Every action to help creates new vulnerability. Game mechanic: player must account for the cost of their rescue attempt, not just the rescue itself.

Model distributed air defense via man-portable shoulder-fired missiles. In 2026 Iran CSAR: F-15E ($100M+) brought down by MANPADS ($50K-$200K). Cost asymmetry democratizes air defense — any soldier with a tube threatens any aircraft at low altitude. HH-60W crews 'engaged by every single person with a small arms weapon.' The threat environment is saturated, not concentrated. There is no safe approach corridor.

Plan and execute landings on unprepared surfaces behind enemy lines. In 2026 Iran CSAR: MC-130J landed on abandoned agricultural airstrip (200ft x 3900ft), 14 miles north of Shahreza. Surface was wet and sandy — aircraft became immobilized. Contingency: lighter C-295W from 427th SOS performed extraction instead. Lesson: always have PACE for landing zones. The primary will fail.

Protocol for intentional destruction of own military assets to prevent adversary technology capture. In 2026 Iran CSAR: 2 MC-130J ($75M each) and 4 Little Birds destroyed in-situ rather than left for Iran to exploit. Total equipment sacrifice: >$200M. Game mechanic: player may need to destroy own tools to prevent intelligence compromise. Every piece of equipment can become a liability if captured.

Model how financial incentives weaponize civilian populations. Iran offered ~$60,000 bounty for WSO capture — 12x average annual income. This transforms every civilian from neutral observer to potential threat motivated by reward. Game mechanic: bounty level determines civilian hostility, creates 'everyone is a threat' environment where even helpful-seeming encounters may be traps.

Coordinate 155 aircraft across 5 types (bombers, fighters, tankers, rescue, special ops) in a single CSAR operation. Includes DEVGRU, Delta Force, AFSOC, 160th SOAR, CIA, plus Israeli intelligence support. Each element has different capabilities, rules, communication systems, and command authorities. This is maximum coordination complexity — the entire force structure activated for 2 people.

Structure game narrative in three simultaneous layers: (1) Human Layer — individual survival, injury, fear, faith, 'God is good'; (2) Machine Layer — 155 aircraft, JPRC coordination, electronic warfare, CIA deception, 7 diversionary strikes; (3) Civilization Layer — why spend $200M+ for 2 people, what that says about values, the compass that guides the decision. Player experiences all three, but emotional weight comes from the human layer.

Model how the rank/seniority of isolated personnel changes rescue calculus. In 2026 Iran CSAR: WSO was colonel rank — carrier of classified knowledge about force structure, operations plans, capabilities. Capture = intelligence catastrophe beyond human cost. Different ranks create different strategic pressure: lieutenant (moral imperative) vs. colonel (strategic necessity + moral imperative). Enemy motivation also changes with rank.

Model civilian population not as background but as active gameplay element. In 2026 Iran CSAR: Bakhtiari nomads (terrain expertise, hunting rifles) hunted WSO. $60,000 bounty motivated civilian search. CIA used local civilian assets to help locate WSO. A shepherd could save you or sell your position. Game mechanic: every civilian NPC has motivations (bounty, loyalty, fear, greed, compassion) that determine whether they help, ignore, or betray.

Define a product's motion language as structured spec: timing (200/300/600ms tiers), easing (in/out/spring), distance (in rem), and meaning (entry/exit/attention/error). Prevents ad-hoc animations that feel random. Output: motion_spec.json consumable by Framer Motion, GSAP, or plain CSS transitions. Includes persona-matched presets (Apple Minimal: slow, long-easing; Spotify Energy: fast, bouncy; SpaceX Future: decisive, material-physics).

Given a DESIGN.md color palette, compute every meaningful foreground/background pair's WCAG 2.2 contrast ratio and emit pass/fail against AA (body ≥4.5:1, large ≥3:1) and AAA (body ≥7:1, large ≥4.5:1). Critical for LIGHT HOPE projects serving older audiences (m510 parents, m504 health, m511 cancer). Output: contrast_matrix.json + top-3 fixes.

Given a light-mode DESIGN.md token set, auto-generate a WCAG-valid dark partner palette using OKLCH perceptual color space (not HSL flip). Preserves brand hue identity while swapping luminance so brand recognition survives dark mode. Handles semantic tokens (success/warning/danger) with elevated-surface ramps (Material 3 elevation model). Output: dark_tokens.css + tailwind.config patch.

Analyze a hero screenshot for composition quality: focal clarity (primary CTA visually dominant?), rule-of-thirds alignment, text-image ratio, breathing space, typographic weight distribution, color dominance. Uses computer vision (no AI required: PIL + opencv feature detection). Emits composition_score.json 0-100 + specific fixes ('CTA is 4% of hero area — aim for 8-12%'). Anti-drift tool: runs on every PR changing hero markup.

Cataloged and persona-matched micro-interactions: hover (lift/glow/underline), click (press/haptic sim), focus (ring/outline/glow), load (skeleton/shimmer/progress), error (shake/fade/inline), success (check/confetti/pulse). Each pattern has code snippet (Tailwind + Framer Motion) + ARIA implications + persona recommendations. Output: micro_library.json + 12 ready-to-paste TSX components.

Given brand voice keywords (e.g., 'authoritative + warm + quiet') and target audience, compose a typographic system: display font (from 30 curated editorial serifs/grotesques), body font (from 20 humanist sans), mono font, modular scale (1.125/1.25/1.333), line-height ladder, letter-spacing per size. Inspired by Editorial magazine systems (New Yorker, Atlantic, Foreign Affairs, Monocle) — the m544 WorldViews reference. Output: typography_system.json + @import URL + tailwind fontFamily block.