harness-setup

name	harness-setup
description	[Quality] Use when setting up an agent quality harness with feedforward guides and feedback sensors.

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Prefer the plan-hard skill for planning guidance in this Codex mirror.
• 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 does not receive Claude hook-based doc injection. 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)

Situation-based docs:

• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec/test-case planning or TC mapping: feature-docs-reference.md
• 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: Set up the complete outer agent harness for a greenfield project so all subsequent AI coding agents operate with maximum guidance and earliest-possible quality feedback.

What this produces:

• Feedforward guides: CLAUDE.md/AGENTS.md conventions, architecture docs, pattern catalogs, skill activation rules
• Computational feedback sensors: configured via $linter-setup (linters, formatters, pre-commit hooks, CI gates)
• Inferential feedback sensors: AI review skills wired to lifecycle stages
• Harness inventory: .ai/workspace/harness/harness-inventory.md

When invoked: After $scaffold and $linter-setup in the greenfield workflow. Assumes scaffolding is complete.

What it does NOT do: Install linters or configure formatters — that is $linter-setup's responsibility.

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Activation Guards

Check 1 — Linter-setup prerequisite (BLOCK if missing): Before running any phases, verify $linter-setup has completed by checking for:

• Linter config file at project root (e.g., .eslintrc, pyproject.toml, .editorconfig)
• Pre-commit hook config (e.g., .husky/, .pre-commit-config.yaml)
• CI quality gate definition

If any of these are missing → a direct user question: "$linter-setup appears incomplete. Computational feedback sensors must be in place before harness setup. Run $linter-setup first, then return here?" BLOCK Phase A/B/C/D/E until linter-setup verification passes.

Check 2 — Existing harness inventory: Check for .ai/workspace/harness/harness-inventory.md

• If found → a direct user question: "Harness inventory already exists — re-run to enhance existing harness, or skip?"
• Do NOT block on CLAUDE.md/AGENTS.md presence — those are feedforward guides this skill may enhance, not signals to skip

---

Phase A — Stack Detection

Read from: plan.md frontmatter → architecture-design report → tech-stack-comparison report.

Extract:

• Primary language(s) and framework(s)
• Test framework and test runner
• CI platform (GitHub Actions, GitLab CI, Azure Pipelines, etc.)
• Package manager and monorepo structure (if any)
• Module system and build tooling

Write detection result to .ai/workspace/harness/stack-profile.md.

If any field undetectable → a direct user question to confirm before proceeding.

---

Phase B — Feedforward Guide Setup (Inferential)

For each guide type, check if it exists; if not, create or enhance:

1. CLAUDE.md / AGENTS.md — Architecture conventions

• Add section: "Architecture Patterns" — document the patterns chosen in $architecture-design (e.g., Clean Architecture, CQRS, Repository)
• Add section: "Anti-Patterns" — explicit list of patterns to avoid for this stack
• Add section: "Naming Conventions" — language-idiomatic conventions for this project
• Add section: "Module Boundaries" — which layers may import which; dependency direction rules

2. Skill activation rules

• Document in CLAUDE.md which skills auto-activate for common task types in this stack
• Example: "When modifying domain entities → activate $review-domain-entities"
• Example: "Before any commit → run $code-review"

3. Architecture notes

• Create docs/architecture/ with:
  • bounded-contexts.md — domain boundaries and ownership
  • dependency-rules.md — allowed import directions between layers
  • naming-conventions.md — project-specific naming for files, classes, functions

4. Pattern catalog

• Create docs/architecture/pattern-catalog.md
• Document each pattern chosen in $architecture-design with DO/DON'T examples
• Anchor to actual project files once scaffolding produces them

Present list of guides created/updated via a direct user question: "Feedforward guides above will be created/enhanced. Confirm or adjust?"

---

Phase C — Computational Feedback Sensors

Confirm $linter-setup has completed:

• Check for linter config file at project root (e.g., .eslintrc, pyproject.toml, .editorconfig)
• Check for pre-commit hook config (e.g., .husky/, .pre-commit-config.yaml)
• Check for CI quality gate definition

If any missing → invoke $linter-setup before continuing.

Output: confirmation that computational sensors are in place, with file paths listed.

---

Phase D — Inferential Feedback Sensors

Configure which AI review skills fire at each lifecycle stage. Present to user via a direct user question: "Which inferential sensors should be mandatory vs optional for this project?"

Pre-implementation (planning gate):

• $why-review — validate design rationale before committing to implementation approach

Pre-commit (lightweight review):

• Document in CLAUDE.md: run $code-review before committing significant changes

Post-implementation (domain model changes):

• $review-domain-entities — when domain entity files are in the changeset

Pre-release (mandatory gates):

• $sre-review — reliability and operational readiness
• $security — security review before production release

Recurring drift detection:

• $scan-codebase-health — schedule quarterly (or on CI schedule) to detect drift

Add the agreed sensor configuration to CLAUDE.md under "## Review Gates".

---

Phase E — Behaviour Harness (Spec + Test Strategy)

Define the project's behaviour harness plan:

Functional spec format:

• a direct user question: "Feature documentation format?" Options: feature-docs (17-section), TDD specs only, lightweight ADRs
• Establish docs/business-features/ or equivalent spec home

Test strategy pyramid:

• Unit: pure functions, domain entities, business logic (no I/O)
• Integration: subcutaneous CQRS tests, repository tests with real DB
• E2E: critical user journeys only (not full coverage — too slow)

Approved fixtures pattern:

• Pre-seed reference/lookup data as approved snapshots
• Integration tests are additive (never delete/reset data)

Coverage threshold:

• a direct user question: "Minimum test coverage threshold for CI gate?" (Recommended: 80% line coverage)
• Add threshold to CI configuration (computational sensor)

Document agreed test strategy to docs/architecture/test-strategy.md.

---

Phase F — Harness Inventory Report

Write .ai/workspace/harness/harness-inventory.md:

# Harness Inventory

Generated: {date}
Stack: {detected stack from Phase A}

## Feedforward Guides

| Type          | File/Skill                           | Purpose                         |
| ------------- | ------------------------------------ | ------------------------------- |
| Inferential   | CLAUDE.md §Architecture Patterns     | Shapes AI architectural choices |
| Inferential   | CLAUDE.md §Anti-Patterns             | Prevents known bad patterns     |
| Inferential   | docs/architecture/pattern-catalog.md | DO/DON'T examples per pattern   |
| Computational | .editorconfig                        | Cross-IDE consistency           |

## Feedback Sensors — Computational

| Stage      | Tool/Hook         | What it catches                 |
| ---------- | ----------------- | ------------------------------- |
| Pre-commit | {linter}          | Style violations, common errors |
| Pre-commit | {formatter}       | Code formatting drift           |
| CI         | {type-checker}    | Type errors                     |
| CI         | {static-analyzer} | Security, complexity, dead code |

## Feedback Sensors — Inferential

| Stage               | Skill/Agent             | What it catches                |
| ------------------- | ----------------------- | ------------------------------ |
| Pre-implementation  | $why-review             | Design rationale gaps          |
| Pre-commit          | $code-review            | Convention drift, logic errors |
| Post-implementation | $review-domain-entities | Domain model quality           |
| Pre-release         | $sre-review             | Operational readiness          |
| Pre-release         | $security               | Security vulnerabilities       |

## Open Gaps

| Area                     | Reason   | Risk           |
| ------------------------ | -------- | -------------- |
| {area not yet harnessed} | {reason} | {LOW/MED/HIGH} |

Present inventory to user for review via a direct user question.

---

Next Steps

a direct user question:

• "$cook (Recommended)" — Begin implementing the project plan with full harness in place
• "$why-review" — Review harness design rationale before proceeding
• "Skip" — Proceed manually without workflow guidance

---

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Harness Engineering — An outer agent harness has two jobs: raise first-attempt quality + provide self-correction feedback loops before human review.

Controls split:

Axis	Type	Examples	Frequency
Feedforward	Computational	.editorconfig, strict compiler flags, enforced module boundaries	Always-on
Feedforward	Inferential	CLAUDE.md conventions, skill prompts, architecture notes, pattern catalogs	Always-on
Feedback	Computational	Linters, type checks, pre-commit hooks, ArchUnit/arch-fitness tests, CI gates	Pre-commit → CI
Feedback	Inferential	$code-review skill, $sre-review, $security, LLM-as-judge passes	Post-commit → CI

Three harness types:

1. Maintainability — Complexity, duplication, coverage, style. Easiest: rich deterministic tooling.
2. Architecture fitness — Module boundaries, dependency direction, performance budgets, observability conventions.
3. Behaviour — Functional correctness. Hardest: requires approved fixtures or strong spec-first discipline.

Keep quality left: pre-commit sensors fire first (cheap), CI sensors fire second, post-review last (expensive).

Research-driven: Never hardcode tool choices. Detect tech stack → research ecosystem → present top 2-3 options → user decides. Enforce strictest defaults; loosen only with explicit approval.

Harnessability signals: Strong typing, explicit module boundaries, opinionated frameworks = easier to harness. Treat these as greenfield architectural choices, not just style preferences.

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

MUST ATTENTION never auto-decide feedforward guide content — present draft and confirm with a direct user question MUST ATTENTION verify $linter-setup completed before Phase C passes MUST ATTENTION write harness-inventory.md incrementally (append after each phase) — never hold in memory MUST ATTENTION harness is a living document — update inventory when new sensors are added later

[TASK-PLANNING] Before acting, analyze task scope and break it into small todo tasks using task tracking.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/hooks/lib/prompt-injections.cjs + .claude/.ck.json

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

1. DETECT: Match prompt against workflow catalog
2. ANALYZE: Find best-match workflow AND evaluate if a custom step combination would fit better
3. ASK (REQUIRED FORMAT): Use a direct user question with this structure:
   • Question: "Which workflow do you want to activate?"
   • Option 1: "Activate [BestMatch Workflow] (Recommended)"
   • Option 2: "Activate custom workflow: [step1 → step2 → ...]" (include one-line rationale)
4. ACTIVATE (if confirmed): Call $workflow-start <workflowId> for standard; sequence custom steps manually
5. CREATE TASKS: task tracking for ALL workflow steps
6. EXECUTE: Follow each step in sequence [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 — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — 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.

Learned Lessons

Lessons Learned

[CRITICAL] Hard-won project debugging/architecture rules. MUST ATTENTION apply BEFORE forming hypothesis or writing code.

Quick Summary

Goal: Prevent recurrence of known failure patterns — debugging, architecture, naming, AI orchestration, environment.

Top Rules (apply always):

• MUST ATTENTION verify ALL preconditions (config, env, DB names, DI regs) BEFORE code-layer hypothesis
• MUST ATTENTION fix responsible layer — NEVER patch symptom sites with caller-specific defensive code
• MUST ATTENTION use ExecuteInjectScopedAsync for parallel async + repo/UoW — NEVER ExecuteUowTask
• MUST ATTENTION name by PURPOSE not CONTENT — adding member forces rename = abstraction broken
• MUST ATTENTION persist sub-agent findings incrementally after each file — NEVER batch at end
• MUST ATTENTION Windows bash: verify Python alias (where python/where py) — NEVER assume python/python3 resolves

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Debugging & Root Cause Reasoning

• [2026-04-11] Holistic-first: verify environment before code. Failure → list ALL preconditions (config, env vars, DB names, endpoints, DI regs, credentials, permissions, data prerequisites) → verify each via evidence (grep/cat/query) BEFORE code-layer hypothesis. Worst rabbit holes: diving nearest layer while bug sits elsewhere — e.g., hours debugging "sync timeout", real cause: test appsettings pointing wrong DB. Cheapest check first.
• [2026-04-01] Ask "whose responsibility?" before fixing. Trace: bug in caller (wrong data) or callee (wrong handling)? Fix responsible layer — NEVER patch symptom site masking real issue.
• [2026-04-01] Trace data lifecycle, not error site. Follow data: creation → transformation → consumption. Bug usually where data created wrong, not consumed.
• [2026-04-01] Code is caller-agnostic. Functions/handlers/consumers don't know who invokes them. Comments/guards/messages describe business intent — NEVER reference specific callers (tests, seeders, scripts).

Architecture Invariants

• [2026-03-31] ParallelAsync + repo/UoW MUST use ExecuteInjectScopedAsync, NEVER ExecuteUowTask. ExecuteUowTask creates new UoW but reuses outer DI scope (same DbContext) — parallel iterations sharing non-thread-safe DbContext silently corrupt data. ExecuteInjectScopedAsync creates new UoW + new DI scope (fresh repo per iteration).
• [2026-03-31] Bus message naming MUST include service name prefix — core services NEVER consume feature events. Prefix declares schema ownership (AccountUserEntityEventBusMessage = Accounts owns). Core services (Accounts, Communication) are leaders. Feature services (Growth, Talents) sending to core MUST use {CoreServiceName}...RequestBusMessage — never define own event for core to consume.

Naming & Abstraction

• [2026-04-12] Name PURPOSE not CONTENT — "OrXxx" anti-pattern. HrManagerOrHrOrPayrollHrOperationsPolicy names set members, not what it guards. Add role → rename = broken abstraction. Rule: names express DOES/GUARDS, not CONTAINS. Test: adding/removing member forces rename? YES = content-driven = bad → rename to purpose (e.g., HrOperationsAccessPolicy). Nuance: "Or" fine in behavioral idioms (FirstOrDefault, SuccessOrThrow) — expresses HAPPENS, not membership.

Environment & Tooling

• [2026-04-20] Windows bash: NEVER assume python/python3 resolves — verify alias first. Python may not be in bash PATH under those names. Check: where python / where py. Prefer py (Windows Python Launcher) for one-liners, node if JS alternative exists.

Test-specific lessons → docs/project-reference/integration-test-reference.md Lessons Learned section. Production-code anti-patterns → docs/project-reference/backend-patterns-reference.md Anti-Patterns section. Generic debugging/refactoring reminders → System Lessons in .claude/hooks/lib/prompt-injections.cjs.

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Closing Reminders

• IMPORTANT MUST ATTENTION holistic-first: verify ALL preconditions (config, env, DB names, endpoints, DI regs) BEFORE code-layer hypothesis — cheapest check first
• IMPORTANT MUST ATTENTION fix responsible layer — NEVER patch symptom site; trace caller (wrong data) vs callee (wrong handling), fix root owner
• IMPORTANT MUST ATTENTION parallel async + repo/UoW → ALWAYS ExecuteInjectScopedAsync, NEVER ExecuteUowTask (shared DbContext = silent data corruption)
• IMPORTANT MUST ATTENTION bus message prefix = schema ownership; feature services NEVER define events for core services — use {CoreServiceName}...RequestBusMessage
• IMPORTANT MUST ATTENTION name by PURPOSE — adding/removing member forces rename = broken abstraction
• IMPORTANT MUST ATTENTION sub-agents MUST write findings after each file/section — NEVER batch all findings into one final write
• IMPORTANT MUST ATTENTION Windows bash: NEVER assume python/python3 resolves — run where python/where py first, use py launcher or node

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
6. Auto-fix gate: "Could $code-review/$code-simplifier/$security/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [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.