Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
[Dev Data] Use when you need to implement or enhance test data seeders that simulate QC happy-path scenarios via application-layer commands.
Codex compatibility note:
Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
User-question prompts mean to ask the user directly in Codex.
Ignore Claude-specific mode-switch instructions when they appear.
Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
If a required step/tool cannot run in this environment, stop and ask the user before adapting.
Codex Project-Reference Loading (No Hooks)
Codex uses static project-reference loading instead of runtime-injected project docs.
When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.
Always read:
docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
docs/project-reference/lessons.md (always-on guardrails and anti-patterns)
Missing/stale context route: If docs/project-config.json, the docs index, lessons.md, CLAUDE.md, AGENTS.md, or any task-required reference doc is missing or stale, auto-run $project-init or the narrow setup route ($project-config, $docs-init, $scan-all, $scan --target=<key>, $claude-md-init) before ordinary project-specific work. If Codex mirrors or AGENTS.md are missing/stale, ask the user to run $sync-codex; do not auto-run it.
Spec authoring, docs/specs/ pathing, or TC format: feature-spec-reference.md, spec-system-reference.md, spec-principles.md
Behavior/public-contract changes or spec-test-code sync: workflow-spec-test-code-cycle-reference.md plus the spec docs above
Derived spec indexes/ERDs/reimplementation guides: spec-system-reference.md and source Feature Specs under docs/specs/
Integration test implementation/review: integration-test-reference.md
E2E test implementation/review: e2e-test-reference.md
Code review/audit work: code-review-rules.md plus domain docs above based on changed files
Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.
[BLOCKING] Execute skill steps in declared order. NEVER skip, reorder, or merge steps without explicit user approval.
[BLOCKING] Before each step or sub-skill call, update task tracking: set in_progress when step starts, set completed when step ends.
[BLOCKING] Every completed/skipped step MUST include brief evidence or explicit skip reason.
[BLOCKING] If Task tools are unavailable, create and maintain an equivalent step-by-step plan tracker with the same status transitions.
Quick Summary
Goal: Implement or enhance test data seeders that create realistic, idempotent, valid test data through application-layer commands (NEVER direct DB writes) โ simulating QC happy-path scenarios without corrupting domain state.
Summary:
Seeders orchestrate the real app pipeline: invoke application-layer commands (which own validation, domain logic, and event side-effects) โ never repo/DB inserts for domain entities, never duplicate command logic in the seeder.
Four non-negotiable gates in order: (1) environment gate as the FIRST check, (2) count-before-seed idempotency, (3) loop from existing_count to target_count (never 0), (4) scoped DI per iteration โ a shared scope silently corrupts the DbContext/session.
Discover the project's seeder base class, env gate key, and count config key in Step 1 with file:line evidence; read the count multiplier from config and never hardcode it (zero โ no-op).
Always pre-read docs/project-reference/seed-test-data-reference.md + project-config Data Seeders group, then close with a fresh zero-memory code-reviewer round; re-review fully only after a validated fix.
Workflow:
Phase 0 โ Detect seeder task type (new / enhance / fix)
Step 4 โ Implement using language-agnostic algorithm
Step 5 โ Validate against universal rules
Review โ Fresh sub-agent review round
Key Rules:
MUST ATTENTION read docs/project-reference/seed-test-data-reference.md and docs/project-config.json (Data Seeders context group) before writing any seeder changes
NEVER call repository/DB directly for domain data โ use application-layer commands
NEVER duplicate command logic โ seeder orchestrates, commands own validation
ALWAYS gate by environment first; ALWAYS check count before seeding
ALWAYS read count multiplier from config (NEVER hardcode)
ALWAYS loop from existing_count to target_count for restart-safety
Restart-Safe โ Idempotency handles restarts: existing count found โ seeds only missing remainder.
Spec-Consistent (Spec-Loop Discipline โ tailored) โ Seeders are orchestration, NOT business logic, so property/metamorphic generation and the MUTATION-SCORE gate are N/A here โ do not force them. Apply the dual-feedback half: every seeded scenario MUST stay consistent with the ยง5 invariants (commands own validation; a seeder that produces state violating an invariant is a bug, not a fixture). If a seeder encodes a domain rule โ a required precondition, a status/relationship the scenario assumes, a business default โ that rule belongs in the spec, not silently in the seeder: feed it into BOTH the spec (the rule) AND, where it is testable, the tests โ never a seeder-only fix.
Protocol
Step 1: Discover Seeder Patterns
Search for project seeder conventions:
# Search configured source roots using the repository's discovered seed-data naming conventions
rg "{configured-seeder-interface-or-base-patterns}|seeder|SeedData|DataSeed" {configured-source-roots} -l
Confirm dev config has both env gate key and count key. If absent, add following project's dev config convention. โ why: missing keys silently disable the gate or count, producing no-op or unbounded seeding.
Step 2: Feature Scope Analysis
Identify before writing any code:
Feature area โ domain entity/aggregate being seeded
Exists โ enhance with new scenarios, do NOT break existing ones
Absent โ create following discovered base class pattern
Step 4: Implement
Algorithm (language-agnostic):
seeder():
if not is_development_environment(): return
if not seed_enabled_in_config(): return
target = config.get("SeedCount")
if target <= 0: return
existing = count_by_seeder_marker()
if existing >= target: return
for i from existing to target:
call_application_command(build_scenario_input(i))
Seeder marker โ stable predicate identifying seeded vs user data:
Email prefix, created-by field, name prefix, or dedicated boolean flag
MUST be deterministic across restarts
Step 5: Validate
MUST ATTENTION verify all before complete:
MUST ATTENTION environment gate is FIRST check โ file:line evidence required
MUST ATTENTION count-before-seed idempotency gate present โ file:line evidence
MUST ATTENTION loop starts at existing_count, not 0 โ file:line evidence
MUST ATTENTION only application-layer commands used for domain entities โ NEVER repo/DB
MUST ATTENTION no business logic or validation duplicated in seeder
MUST ATTENTION seeder registered via project DI mechanism โ file:line evidence
MUST ATTENTION count config key read correctly (zero โ no-op, NEVER hardcoded)
MUST ATTENTION scoped DI per iteration โ shared scope = DbContext/session corruption
Sub-Agent Routing
Task
Sub-Agent
When
Discover seeders + commands across large codebase
general-purpose
Steps 1-2
Review seeder compliance
code-reviewer
Round 1 post-implementation
Seeder handles credentials/PII
security-auditor
Security-sensitive patterns
Seeder runs 1000+ records
performance-optimizer
Performance-intensive
All sub-agent prompts MUST include:
Graph DB active. After grep finds key files, run:
python .claude/scripts/code_graph trace <file> --direction both --json
Pattern: grep โ trace โ grep verify.
Use project's scoped DI per iteration (prevents DbContext corruption)
Batch-all-then-write sub-agent findings
Persist findings per file; NEVER batch at end
Review Loop
Round 1: After implementation, spawn fresh code-reviewer sub-agent with zero memory of implementation:
Review seeder at [file:path]. Verify with file:line evidence for each:
1. Environment gate is FIRST check
2. Idempotency: count-before-seed pattern present
3. Loop starts at existing_count not 0
4. Zero application-layer command bypasses (direct repo/DB = FAIL)
5. No hardcoded count โ config key read
6. Scoped DI per iteration
Report: PASS or FAIL with file:line for each finding.
Fix loop: If FAIL โ validate findings โ fix validated findings โ restart full review from first phase. When restarted review uses sub-agents, NEVER reuse them across rounds. If same blocker repeats across 3 full invocations with no progress, escalate to user.
NEVER fix unvalidated findings. Do not spawn a fresh sub-agent only to re-review known findings before validation/fix.
Workflow Recommendation
MUST ATTENTION โ NOT IN WORKFLOW YET: Use a direct user question:
Execute $seed-test-data directly โ run this skill standalone
Next Steps
MUST ATTENTION after completing: use a direct user question โ do NOT skip:
"$workflow-review-changes (Recommended)" โ review all changes before commit
"$integration-test" โ write tests verifying idempotency and count compliance
"Skip, continue manually" โ user decides
[IMPORTANT] task tracking for ALL tasks BEFORE starting. For simple tasks, ask user whether to skip.
Critical Thinking Mindset โ Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act.
Anti-hallucination: Never present guess as fact โ cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence โ certainty without evidence root of all hallucination.
Understand Code First โ HARD-GATE: Do NOT write, plan, or fix until you READ existing code.
Search 3+ similar patterns (grep/glob) โ cite file:line evidence
Read existing files in target area โ understand structure, base classes, conventions
Run python .claude/scripts/code_graph trace <file> --direction both --json when .code-graph/graph.db exists
Map dependencies via connections or callers_of โ know what depends on your target
Write investigation to .ai/workspace/analysis/ for non-trivial tasks (3+ files)
Re-read analysis file before implementing โ never work from memory alone. โ why: long context drifts from the file; the file is ground truth
NEVER invent new patterns when existing ones work โ match exactly or document deviation. โ why: divergent patterns fragment the codebase and slow every future reader
Evidence-Based Reasoning โ Speculation is FORBIDDEN. Every claim needs proof.
Cite file:line, grep results, or framework docs for EVERY claim
Declare confidence: >80% act freely, 60-80% verify first, <60% DO NOT recommend
Cross-service validation required for architectural changes
"I don't have enough evidence" is valid and expected output
BLOCKED until:- [ ] Evidence file path (file:line) - [ ] Grep search performed - [ ] 3+ similar patterns found - [ ] Confidence level stated
Forbidden without proof: "obviously", "I think", "should be", "probably", "this is because"
If incomplete โ output: "Insufficient evidence. Verified: [...]. Not verified: [...]."
AI Mistake Prevention โ Failure modes to avoid on every task:
Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting.
Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing.
Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first.
Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done.
Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect.
Assume existing values are intentional โ ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history.
Surface ambiguity before acting โ don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk.
Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.
IMPORTANT MUST ATTENTION search 3+ existing patterns and read code BEFORE writing any seeder.
MUST ATTENTION cite file:line for every claim; declare confidence; "I don't have enough evidence" is valid output.
MUST ATTENTION apply critical + sequential thinking โ every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.
MUST ATTENTION apply AI mistake prevention โ verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.
Prompt-Enhance Closing Anchors
IMPORTANT MUST ATTENTION follow declared step order for this skill; NEVER skip, reorder, or merge steps without explicit user approval
IMPORTANT MUST ATTENTION for every step/sub-skill call: set in_progress before execution, set completed after execution
IMPORTANT MUST ATTENTION every skipped step MUST include explicit reason; every completed step MUST include concise evidence
IMPORTANT MUST ATTENTION if Task tools unavailable, maintain an equivalent step-by-step plan tracker with synchronized statuses
Closing Reminders
IMPORTANT MUST ATTENTION Goal: Implement/enhance seeders creating realistic, idempotent, valid test data through application-layer commands (NEVER direct DB writes) โ simulate QC happy-path scenarios without corrupting domain state.
Protocols in force (concise digest of the SYNC/shared blocks this skill carries):
Understand Code First: ALWAYS search 3+ patterns and read code before writing.
Evidence: MUST ATTENTION cite file:line per claim; declare confidence; "insufficient evidence" valid.
AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.
IMPORTANT MUST ATTENTION NEVER call repo/DB directly for domain data โ use application-layer commands โ why: bypassing the command pipeline skips validation, domain logic, and event side-effects, producing invalid state that passes silently
IMPORTANT MUST ATTENTION ALWAYS gate by environment FIRST, then ALWAYS check count before seeding โ why: env gate prevents prod corruption; count gate is the idempotency guarantee
IMPORTANT MUST ATTENTION loop from existing_count to target_count โ NEVER from 0 โ why: looping from 0 re-seeds on every restart and breaks restart-safety
IMPORTANT MUST ATTENTION scoped DI per iteration โ shared DI scope = silent DbContext/session corruption
IMPORTANT MUST ATTENTION ALWAYS read count multiplier from the discovered config key โ NEVER hardcode (zero โ no-op, never unbounded loop)
IMPORTANT MUST ATTENTION NEVER duplicate command logic in the seeder โ seeder provides realistic inputs, commands own validation/domain/events
IMPORTANT MUST ATTENTION every seeded scenario MUST stay consistent with the ยง5 universal invariants; if a seeder encodes a domain rule (precondition, status, default) feed it into the spec โ and tests where testable โ NEVER a seeder-only fix โ why: a hidden rule in a seeder drifts from the spec and breaks future readers
IMPORTANT MUST ATTENTION Evidence gate: cite file:line for the env gate, count gate, loop start, DI scope, and seeder registration โ confidence >80% to act, <60% DO NOT recommend; "Insufficient evidence" is valid output
IMPORTANT MUST ATTENTION search 3+ existing seeder patterns and READ them before writing โ match the discovered base class / env-gate / count-key conventions exactly; verify the copied pattern shares the same preconditions (base class, scope, lifetime) before reuse
IMPORTANT MUST ATTENTION read docs/project-reference/seed-test-data-reference.md + docs/project-config.json (Data Seeders group) BEFORE any seeder change โ project conventions override generic defaults
IMPORTANT MUST ATTENTION task tracking โ break all work into tasks BEFORE starting; transition one task at a time, evidence per completed step
IMPORTANT MUST ATTENTION close with a fresh zero-memory code-reviewer round; full re-review is required ONLY after a validated fix cycle โ a clean review pass ENDS the review; NEVER fix unvalidated findings
Anti-Rationalization:
Evasion
Rebuttal
"Simple seeder, skip review loop"
Idempotency bugs are silent. Run Round 1 always.
"Already know the base class"
Show file:line. No proof = no knowledge.
"Environment gate is obvious"
Verify it's FIRST check with file:line evidence.
"Just hardcode count for now"
NEVER โ config key required. Find it in Step 1.
"Seeder can validate this quickly"
NEVER duplicate logic โ command owns validation; seeder feeds inputs.
"Skip the reference docs, I know seeders"
Project conventions override generic patterns. Read them first.
"No graph.db, skip trace"
Use grep-only trace. Still run 3+ pattern search.
"Existing scenarios look fine, skip enhance"
Read all scenarios; enhancement may conflict โ verify first.
[TASK-PLANNING] Before acting, break task into small todo tasks using task tracking.
IMPORTANT MUST ATTENTION NEVER direct repo/DB writes for domain data ยท ALWAYS env-gate FIRST then count-gate ยท file:line evidence for every gate (confidence >80%).
[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol โ MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.
Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. For spec, test-case, behavior-change, public-contract, or docs/specs/ work, route through the local spec docs named by the docs index: feature-spec-reference.md, spec-system-reference.md, spec-principles.md, and workflow-spec-test-code-cycle-reference.md when specs/tests/code must stay synchronized. If either file or a required reference doc is missing or stale, auto-run $project-init (or the narrow lower-level route such as $project-config, $docs-init, $scan-all, or $scan --target=<key>) before ordinary project-specific work. Any supported AI tool may execute when this shared context and local docs are available.
DETECT: If the prompt starts with an explicit slash skill/workflow command, execute it directly. Otherwise match the prompt against the workflow catalog and skill list.
ANALYZE: Choose the best option: execute directly, invoke a skill, activate a standard workflow, or compose a custom step combination.
AUTO-SELECT: Pick the best option yourself. Do not ask the user to choose between direct execution, skill, standard workflow, or custom workflow.
ACTIVATE: For a selected workflow, call $start-workflow <workflowId>; for a selected skill, invoke that skill; for a custom workflow, sequence custom steps directly; for direct execution, proceed with the task.
CREATE TASKS: task tracking for ALL workflow/skill/custom steps before execution when the selected path has multiple steps.
EXECUTE: Advance per the Workflow Step Advancement & Parallel Phases rule in your context instructions โ model-driven; a sub-agent completion advances a step identically to an inline call; a parallel-phase group is an all-return barrier (advance only after ALL members return, never serialize it)
Trace every requirement or invariant through decision, task, TC/test, source evidence, and docs/spec update.
Treat code-to-spec extraction as reference-only until accepted by the canonical spec owner.
Any supported AI tool may plan, implement, review, or verify with synced context; using multiple tools is optional.
Update .claude source first, then sync generated mirrors; do not manually edit .agents, .codex, or AGENTS.md. โ why: mirrors are generated artifacts; hand-edits are overwritten on the next sync
If docs/project-config.json, root instruction files, or a required project-reference doc is missing or stale, auto-run $project-init or the narrow lower-level route before ordinary project-specific work.
Active reference:shared/sdd-artifact-contract.md in the active skills root.
SYNC:ai-sdd-artifact-contract:reminder
MANDATORY Apply shared/sdd-artifact-contract.md; keep reusable AI-SDD in .claude and local rules in project docs.
MANDATORY Code-to-spec extraction is reference-only until canonical acceptance; any supported AI tool may execute with synced context.
MANDATORY Update .claude source before syncing generated mirrors; do not manually edit .agents, .codex, or AGENTS.md.
MANDATORY Missing or stale project config, root instruction files, or required reference docs route project-specific work through $project-init or the narrow setup route automatically.
[TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.
[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement โ MANDATORY. Do not skip.
Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".
Extract lessons โ ROOT CAUSE ONLY, not symptom fixes:
Name the FAILURE MODE (reasoning/assumption failure), not symptom โ "assumed API existed without reading source" not "used wrong enum value".
Generality test: does this failure mode apply to โฅ3 contexts/codebases? If not, abstract one level up.
Write as a universal rule โ strip project-specific names/paths/classes. Useful on any codebase.
Consolidate: multiple mistakes sharing one failure mode โ ONE lesson.
Recurrence gate: "Would this recur in future session WITHOUT this reminder?" โ No โ skip $learn.
BOTH gates pass โ ask user to run $learn.
[CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act.
Anti-hallucination principle: Never present guess as fact โ cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence โ certainty without evidence root of all hallucination.
AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows.
Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass.
Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.
Common AI Mistake Prevention (System Lessons)
Re-read files after context compaction. Edit requires prior Read in same context; compaction wipes read state. Re-read before editing.
Grep for old terms after bulk replacements. AI over-trusts find/replace completeness. Grep full repo after bulk edits for missed refs in docs/configs/catalogs.
Check downstream references before deleting. Deletions cascade doc/code staleness. Map referencing files before removal.
After memory loss, check existing state before creating new. Compaction wipes prior-work memory. Query current state to resume โ never blindly duplicate.
Verify AI-generated content against actual code. AI hallucinates APIs, class names, method signatures. Grep to confirm existence before documenting/referencing.
Trace full dependency chain after edits. Changing a definition misses downstream consumers. Trace the full chain.
When renaming, grep ALL consumer file types. Some file types silently ignore missing refs (no compile error). Search code, templates, configs, generated files.
Trace ALL code paths when verifying correctness. Code existing โ code executing. Trace early exits, error branches, conditional skips โ not just happy path.
Update docs that embed canonical data when source changes. Docs inlining derived data (workflows, schemas, configs) go stale silently. Update all embedding docs alongside source.
Verify sub-agent results after context recovery. Background agents may finish while parent compacted โ grep-verify output, don't trust assumed completion.
Cross-check full target list against sub-agent assignments. Parallel sub-agents by category miss boundary items. Reconcile union of assignments against target list before proceeding.
Sub-agents inherit knowledge only from their agent .md definition โ use custom agent types, not built-in Explore. Tool adoption = permission + knowledge + enforcement (numbered workflow step).
Persist sub-agent findings incrementally, not as a final batch. Long sub-agents hit cutoffs before final write โ findings lost. Instruct append-per-section to report file.
When debugging, ask "whose responsibility?" before fixing. Trace caller (wrong data) vs callee (wrong handling). Fix at responsible layer โ never patch symptom site.
Grep ALL removed names after extraction/refactoring. Primary file "done" โ secondary files clean. Grep entire scope for every removed symbol before declaring complete.
Assume existing values are intentional โ ask WHY before changing. Pattern-matching as "wrong" skips context. Before changing any constant/limit/flag: read comments, git blame, surrounding code.
Verify ALL affected outputs, not just the first. One build green โ all green. Multi-stack changes (backend/frontend/tests/docs) require verifying EVERY output.
Evaluate fit before copying a nearby pattern. Closest example โ matching preconditions โ verify the new context shares the same constraints, base classes, scope, lifetime.
Holistic-first debugging โ resist nearest-attention trap. Don't dive into first plausible cause. List EVERY precondition (config, env vars, paths, DB, endpoints, creds, versions, DI, data). Verify each against evidence (grep/query โ not reasoning). Ask "what would falsify this?" โ if nothing, it's not a hypothesis. Most expensive failure: going deeper in "obvious" layer while bug sits in layer never questioned.
Surgical changes โ apply the diff test (context-aware). Two modes: (1) Bug fix โ every line traces to the bug; no restyling; orphan cleanup only for imports YOUR changes made unused. (2) Review/enhancement โ implement improvements AND announce as "Enhancement beyond main request: [what]". Never silently scope-creep. Diff test: "Would this line exist if I wasn't asked to do X?" โ if no, delete or announce.
Surface ambiguity before coding โ don't pick silently. Multiple valid interpretations โ present each with effort: "[Request] could mean (1) [N h], (2) [N h]. Which matters?" List scope/format/volume/constraints assumptions first. If simpler path exists, say so. Never silently pick.
[MANDATORY FIRST ACTION] ALWAYS activate a suitable skill or workflow BEFORE responding. Match task against workflow catalog + skill list; invoke via skill invocation or $start-workflow <workflowId>. NEVER answer or write code before checking. Skip = protocol violation.
Why-Review adversarial mindset โ apply when reviewing any plan, decision, or design. Default SKEPTIC not VALIDATOR: steel-man a rejected alternative, invert each stated reason ("what does it sacrifice?"), stress-test top 2-3 assumptions, run pre-mortem ("ships, fails in 3 months โ what breaks?"), surface 1-2 alternatives author missed. Section presence โ quality; quality = causal reasoning + concrete mitigations + evidence, not "it's better" or "monitor closely".
Front-load report-write in sub-agent prompts for large reviews. Many-file sub-agents hit budget before final write โ findings lost. Design prompts so: (1) report-write is first explicit deliverable, (2) append per-file/section (not batched), (3) scope bounded so reads don't exhaust budget. Truncated mid-sentence with no report file โ spawn narrower scope, don't retry same prompt.
After context compaction, re-verify all prior phase outcomes before continuing. Summaries describe intent, not environment state (git index, filesystem, processes). On resume, FIRST audit: git status, re-read modified files, verify filesystem. Every "completed" claim is an untested hypothesis until evidence confirms.
OOM/memory: check row count before row size. Triage: (1) Unbounded query โ no DB filter for trigger? Push filter to DB; eliminates OOM. (2) Large rows? Projection reduces proportionally. Row reduction > projection in ROI.
Keep domain concepts out of generic/shared/infrastructure layers. Reusable layer (shared library, framework, infra module) must reference NO consumer-specific domain concept โ tenant/customer/product IDs, business entities, feature rules. Leak compiles + runs โ passes review silently while coupling the "reusable" layer to one consumer. Keep shared type domain-free; push domain fields/logic down into the consumer via subclass/composition. โ why: a layer coupled to one consumer's domain is no longer reusable.
