v1.13.12: skills audit + token-tracking fix + codecontext + cap50 + UI cleanups
Multi-topic batch. The big-ticket item is the skills audit; the rest are smaller patches that compounded during the audit work. ## Skills audit (rules→recipes split) Vendored all 26 skills from /home/samkintop/opt/skills/ into data/skills/ (the boocode-repo-local skill library — see docker-compose change below). Audited via 5 parallel Claude Code agent-teams running the mgechev/skills-best-practices 4-step protocol (Discovery → Logic → Edge Case → self-Architecture-Refinement) per skill, ~2 min wall-clock vs the ~3.7-hour serial estimate. Result: 14 skills surviving (renamed to gerund form, frontmatter matched), 11 deleted (duplicates, BooCode-irrelevant patterns, Claude-already-does- natively), 1 migrated to BOOCHAT.md/BOOCODER.md as an always-true rule (verification-before-completion). Each surviving skill had its description refined to fix specific trigger gaps surfaced by the protocol — 4 real-bug findings landed (dead refs, stale tags, broken sub-file references in the original vendored content). Audit decisions documented in openspec/changes/v1.13.12-skills-audit/ audit-notes.md. Convention codified in BOOCHAT.md/BOOCODER.md "rules vs recipes" sections — future workflow rules go to those files (100% present), recipes stay in data/skills/ (~6% invoke rate in multi-turn per the Codeminer42 measurement). ## Token tracking + stale-stream banner fix (same root cause) ws-frames.ts IsoTimestamp was z.string().min(1) but postgres returns timestamp columns as JS Date objects. Every message_complete / session_updated / chat_updated frame was failing the v1.13.11 Zod gate and being silently dropped. Symptoms: token tracking blank in the UI (no usage frames landed); the 60s no-token-activity timer tripped the stale-stream banner because the frontend's local message state never saw status='streaming' flip to 'complete'. Fix: z.preprocess(v => v instanceof Date ? v.toISOString() : v, z.string().min(1)) applied to the IsoTimestamp primitive. Centralized, no publisher changes, works identically server + web (the parity test still passes). ## Codecontext .codecontextignore auto-install services/codecontext_client.ts now copies the codecontext/.codecontextignore.template into any project's root on the first call to that project if no .codecontextignore exists. One file written per project, idempotent (in-memory Set guard + access-check), silent fallback on read-only project. Stops the upstream empty-source- file parser crash on foreign projects' node_modules — previously required manually copying the template per project. ## Tool-call budget cap 30 → 50 services/inference/budget.ts: BUDGET_READ_ONLY and BUDGET_NO_AGENT bumped to 50 (from 30). BUDGET_NON_READ_ONLY stays at 10 (no write tools landed yet). Real recon sessions were hitting 30 with ~3 turns wasted on codecontext parse failures; legitimate need was ~27, and Architect-class system overviews want deeper recon. Headroom of 20 absorbs failure-retry turns without changing the safety floor — the doom-loop guard (3 identical calls → abort) catches the actual failure mode this cap was guarding against. v1.14 (Phase C outer agent loop) will supersede this via per-agent agent.steps. Throwaway-ish patch but unblocks deeper recon today. ## UI cleanups - ChatPane queued-message dropdown removed. Each queued message now has three buttons: edit (pop back into ChatInput via sendToChat event), force-send (was the dropdown's only useful action), and cancel. Default behavior (send when streaming completes) needs no UI — it's the implicit do-nothing path. - ChatThroughput removed from desktop tab strip (ChatTabBar.tsx). Mobile tab switcher still shows it. ## Plumbing - .gitignore: data/* + !data/AGENTS.md + !data/skills/ negation patterns so the vendored skill library + agent registry become git-tracked while session DB state stays out. - docker-compose.yml: removed /opt/skills:/data/skills override mount. Skills now live in the boocode repo at data/skills/, auditable per-batch. The host-level /opt/skills/ is preserved untouched for any other tools that read from it. - .codecontextignore at repo root: auto-installed when codecontext was first called against /opt/boocode itself; matches the template. - CLAUDE.md: updated to document the v1.13.11 publishFrame wrapper + message_parts table + tool_cost_stats view + DB-integration test pattern + host-side smoke endpoint quirk. (Pre-existing in working tree before this batch; shipped here for completeness.) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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---
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name: systematic-debugging
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description: Use when encountering any bug, test failure, unexpected behavior, build failure, or compile error, before proposing fixes. Also use when asked to debug, investigate, or diagnose an issue.
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---
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# Systematic Debugging
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## Overview
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Random fixes waste time and create new bugs. Quick patches mask underlying issues.
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**Core principle:** ALWAYS find root cause before attempting fixes. Symptom fixes are failure.
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**Violating the letter of this process is violating the spirit of debugging.**
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## The Iron Law
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```
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NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
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```
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If you haven't completed Phase 1, you cannot propose fixes.
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## When to Use
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Use for ANY technical issue:
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- Test failures
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- Bugs in production
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- Unexpected behavior
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- Performance problems
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- Build failures
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- Integration issues
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**Use this ESPECIALLY when:**
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- Under time pressure (emergencies make guessing tempting)
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- "Just one quick fix" seems obvious
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- You've already tried multiple fixes
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- Previous fix didn't work
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- You don't fully understand the issue
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**Don't skip when:**
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- Issue seems simple (simple bugs have root causes too)
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- You're in a hurry (rushing guarantees rework)
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- Manager wants it fixed NOW (systematic is faster than thrashing)
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## The Four Phases
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You MUST complete each phase before proceeding to the next.
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### Phase 1: Root Cause Investigation
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**BEFORE attempting ANY fix:**
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1. **Read Error Messages Carefully**
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- Don't skip past errors or warnings
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- They often contain the exact solution
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- Read stack traces completely
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- Note line numbers, file paths, error codes
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2. **Reproduce Consistently**
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- Can you trigger it reliably?
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- What are the exact steps?
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- Does it happen every time?
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- If not reproducible → gather more data, don't guess
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3. **Check Recent Changes**
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- What changed that could cause this?
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- Git diff, recent commits
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- New dependencies, config changes
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- Environmental differences
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4. **Gather Evidence in Multi-Component Systems**
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**WHEN system has multiple components (CI → build → signing, API → service → database):**
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**BEFORE proposing fixes, add diagnostic instrumentation:**
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```
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For EACH component boundary:
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- Log what data enters component
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- Log what data exits component
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- Verify environment/config propagation
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- Check state at each layer
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Run once to gather evidence showing WHERE it breaks
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THEN analyze evidence to identify failing component
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THEN investigate that specific component
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```
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**Example (multi-layer system):**
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```bash
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# Layer 1: Workflow
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echo "=== Secrets available in workflow: ==="
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echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"
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# Layer 2: Build script
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echo "=== Env vars in build script: ==="
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env | grep IDENTITY || echo "IDENTITY not in environment"
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# Layer 3: Signing script
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echo "=== Keychain state: ==="
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security list-keychains
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security find-identity -v
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# Layer 4: Actual signing
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codesign --sign "$IDENTITY" --verbose=4 "$APP"
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```
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**This reveals:** Which layer fails (secrets → workflow ✓, workflow → build ✗)
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5. **Trace Data Flow**
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**WHEN error is deep in call stack:**
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See `root-cause-tracing.md` in this directory for the complete backward tracing technique.
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**Quick version:**
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- Where does bad value originate?
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- What called this with bad value?
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- Keep tracing up until you find the source
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- Fix at source, not at symptom
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### Phase 2: Pattern Analysis
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**Find the pattern before fixing:**
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1. **Find Working Examples**
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- Locate similar working code in same codebase
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- What works that's similar to what's broken?
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2. **Compare Against References**
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- If implementing pattern, read reference implementation COMPLETELY
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- Don't skim - read every line
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- Understand the pattern fully before applying
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3. **Identify Differences**
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- What's different between working and broken?
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- List every difference, however small
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- Don't assume "that can't matter"
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4. **Understand Dependencies**
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- What other components does this need?
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- What settings, config, environment?
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- What assumptions does it make?
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### Phase 3: Hypothesis and Testing
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**Scientific method:**
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1. **Form Single Hypothesis**
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- State clearly: "I think X is the root cause because Y"
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- Write it down
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- Be specific, not vague
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2. **Test Minimally**
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- Make the SMALLEST possible change to test hypothesis
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- One variable at a time
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- Don't fix multiple things at once
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3. **Verify Before Continuing**
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- Did it work? Yes → Phase 4
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- Didn't work? Form NEW hypothesis
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- DON'T add more fixes on top
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4. **When You Don't Know**
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- Say "I don't understand X"
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- Don't pretend to know
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- Ask for help
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- Research more
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### Phase 4: Implementation
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**Fix the root cause, not the symptom:**
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1. **Create Failing Test Case**
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- Simplest possible reproduction
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- Automated test if possible
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- One-off test script if no framework
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- MUST have before fixing
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- Use the `superpowers:test-driven-development` skill for writing proper failing tests
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2. **Implement Single Fix**
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- Address the root cause identified
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- ONE change at a time
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- No "while I'm here" improvements
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- No bundled refactoring
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3. **Verify Fix**
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- Test passes now?
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- No other tests broken?
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- Issue actually resolved?
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4. **If Fix Doesn't Work**
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- STOP
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- Count: How many fixes have you tried?
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- If < 3: Return to Phase 1, re-analyze with new information
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- **If ≥ 3: STOP and question the architecture (step 5 below)**
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- DON'T attempt Fix #4 without architectural discussion
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5. **If 3+ Fixes Failed: Question Architecture**
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**Pattern indicating architectural problem:**
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- Each fix reveals new shared state/coupling/problem in different place
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- Fixes require "massive refactoring" to implement
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- Each fix creates new symptoms elsewhere
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**STOP and question fundamentals:**
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- Is this pattern fundamentally sound?
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- Are we "sticking with it through sheer inertia"?
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- Should we refactor architecture vs. continue fixing symptoms?
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**Discuss with your human partner before attempting more fixes**
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This is NOT a failed hypothesis - this is a wrong architecture.
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## Red Flags - STOP and Follow Process
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If you catch yourself thinking:
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- "Quick fix for now, investigate later"
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- "Just try changing X and see if it works"
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- "Add multiple changes, run tests"
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- "Skip the test, I'll manually verify"
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- "It's probably X, let me fix that"
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- "I don't fully understand but this might work"
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- "Pattern says X but I'll adapt it differently"
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- "Here are the main problems: [lists fixes without investigation]"
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- Proposing solutions before tracing data flow
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- **"One more fix attempt" (when already tried 2+)**
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- **Each fix reveals new problem in different place**
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**ALL of these mean: STOP. Return to Phase 1.**
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**If 3+ fixes failed:** Question the architecture (see Phase 4.5)
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## your human partner's Signals You're Doing It Wrong
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**Watch for these redirections:**
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- "Is that not happening?" - You assumed without verifying
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- "Will it show us...?" - You should have added evidence gathering
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- "Stop guessing" - You're proposing fixes without understanding
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- "Ultrathink this" - Question fundamentals, not just symptoms
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- "We're stuck?" (frustrated) - Your approach isn't working
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**When you see these:** STOP. Return to Phase 1.
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## Common Rationalizations
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| Excuse | Reality |
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|--------|---------|
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| "Issue is simple, don't need process" | Simple issues have root causes too. Process is fast for simple bugs. |
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| "Emergency, no time for process" | Systematic debugging is FASTER than guess-and-check thrashing. |
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| "Just try this first, then investigate" | First fix sets the pattern. Do it right from the start. |
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| "I'll write test after confirming fix works" | Untested fixes don't stick. Test first proves it. |
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| "Multiple fixes at once saves time" | Can't isolate what worked. Causes new bugs. |
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| "Reference too long, I'll adapt the pattern" | Partial understanding guarantees bugs. Read it completely. |
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| "I see the problem, let me fix it" | Seeing symptoms ≠ understanding root cause. |
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| "One more fix attempt" (after 2+ failures) | 3+ failures = architectural problem. Question pattern, don't fix again. |
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## Quick Reference
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| Phase | Key Activities | Success Criteria |
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|-------|---------------|------------------|
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| **1. Root Cause** | Read errors, reproduce, check changes, gather evidence | Understand WHAT and WHY |
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| **2. Pattern** | Find working examples, compare | Identify differences |
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| **3. Hypothesis** | Form theory, test minimally | Confirmed or new hypothesis |
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| **4. Implementation** | Create test, fix, verify | Bug resolved, tests pass |
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## When Process Reveals "No Root Cause"
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If systematic investigation reveals issue is truly environmental, timing-dependent, or external:
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1. You've completed the process
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2. Document what you investigated
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3. Implement appropriate handling (retry, timeout, error message)
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4. Add monitoring/logging for future investigation
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**But:** 95% of "no root cause" cases are incomplete investigation.
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## Supporting Techniques
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These techniques are part of systematic debugging and available in this directory:
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- **`root-cause-tracing.md`** - Trace bugs backward through call stack to find original trigger
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- **`defense-in-depth.md`** - Add validation at multiple layers after finding root cause
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- **`condition-based-waiting.md`** - Replace arbitrary timeouts with condition polling
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**Related skills:**
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- **superpowers:test-driven-development** - For creating failing test case (Phase 4, Step 1)
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- **superpowers:verification-before-completion** - Verify fix worked before claiming success
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## Real-World Impact
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From debugging sessions:
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- Systematic approach: 15-30 minutes to fix
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- Random fixes approach: 2-3 hours of thrashing
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- First-time fix rate: 95% vs 40%
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- New bugs introduced: Near zero vs common
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