EasyPlatform fix-test

[Implementation] Run test suite and fix issues

install

source · Clone the upstream repo

git clone https://github.com/duc01226/EasyPlatform

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/duc01226/EasyPlatform "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/fix-test" ~/.claude/skills/duc01226-easyplatform-fix-test && rm -rf "$T"

manifest: .claude/skills/fix-test/SKILL.md

source content

[IMPORTANT] Use
TaskCreate
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.

Understand Code First — HARD-GATE: Do NOT write, plan, or fix until you READ existing code.
Search 3+ similar patterns (
grep
/
glob
) — cite
file:line
evidence
Read existing files in target area — 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
— know what depends on your target
Write investigation to
.ai/workspace/analysis/
for non-trivial tasks (3+ files)
Re-read analysis file before implementing — never work from memory alone

NEVER invent new patterns when existing ones work — match exactly or document deviation
BLOCKED until:
- [ ]
Read target files
- [ ]
Grep 3+ patterns
- [ ]
Graph trace (if graph.db exists)
- [ ]
Assumptions verified with evidence

Evidence-Based Reasoning — 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 → output:
"Insufficient evidence. Verified: [...]. Not verified: [...]."

```
docs/project-reference/domain-entities-reference.md
```
— Domain entity catalog, relationships, cross-service sync (read when task involves business entities/models) (content auto-injected by hook — check for [Injected: ...] header before reading)
```
docs/project-reference/integration-test-reference.md
```
— Integration test patterns, fixture setup, seeder conventions, lessons learned (MUST READ before reviewing/writing integration tests)

Estimation — Modified Fibonacci: 1(trivial) → 2(small) → 3(medium) → 5(large) → 8(very large) → 13(epic, SHOULD split) → 21(MUST ATTENTION split). Output
story_points
and
complexity
in plan frontmatter. Complexity auto-derived: 1-2=Low, 3-5=Medium, 8=High, 13+=Critical.

```
docs/test-specs/
```
— Test specifications by module (read existing TCs for expected behavior context when diagnosing failures)

Skill Variant: Variant of
/fix
— test suite failure diagnosis and resolution.

Quick Summary

Goal: Run test suites, analyze failures, and fix the underlying code or test issues.

Workflow:

Run — Execute test suite and capture results
Analyze — Identify failing tests, classify as code bug vs test issue
Fix — Apply targeted fix to code or test

Key Rules:

Debug Mindset: every claim needs
```
file:line
```
evidence
Distinguish between code bugs and flawed test expectations
Run tests again after fix to confirm all pass

Root Cause Debugging — Systematic approach, never guess-and-check.
Reproduce — Confirm the issue exists with evidence (error message, stack trace, screenshot)

Isolate — Narrow to specific file/function/line using binary search + graph trace

Trace — Follow data flow from input to failure point. Read actual code, don't infer.

Hypothesize — Form theory with confidence %. State what evidence supports/contradicts it

Verify — Test hypothesis with targeted grep/read. One variable at a time.
Fix — Address root cause, not symptoms. Verify fix doesn't break callers via graph
connections
NEVER: Guess without evidence. Fix symptoms instead of cause. Skip reproduction step.

Analyze the skills catalog and activate the skills that are needed for the task during the process.

Debug Mindset (NON-NEGOTIABLE)

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Do NOT assume the first hypothesis is correct — verify with actual code traces
Every root cause claim must include
```
file:line
```
evidence
If you cannot prove a root cause with a code trace, state "hypothesis, not confirmed"
Question assumptions: "Is this really the cause?" → trace the actual execution path
Challenge completeness: "Are there other contributing factors?" → check related code paths
No "should fix it" without proof — verify the fix addresses the traced root cause

⚠️ MANDATORY: Confidence & Evidence Gate

MANDATORY IMPORTANT MUST ATTENTION declare

Confidence: X%

with evidence list +

file:line

proof for EVERY claim. 95%+ recommend freely | 80-94% with caveats | 60-79% list unknowns | <60% STOP — gather more evidence.

⚠️ Validate Before Fix (NON-NEGOTIABLE): After root cause analysis + plan creation, MUST ATTENTION present findings + proposed fix to user via
AskUserQuestion
and get explicit approval BEFORE any code changes. No silent fixes.

Reported Issues:

<issues>$ARGUMENTS</issues>

Workflow:

Use
```
tester
```
subagent to compile the code and fix all syntax errors if any.
Use
```
tester
```
subagent to run the tests and report back to main agent.
- External Memory: Write test failure analysis to
```
.ai/workspace/analysis/{test-issue}.analysis.md
```
  . Re-read before fixing.
If there are issues or failed tests, use
```
debugger
```
subagent to find the root cause of the issues, then report back to main agent.
Use
```
planner
```
subagent to create an implementation plan based on the reports, then report back to main agent.
🛑 Present root cause + fix plan →
AskUserQuestion
→ wait for user approval.
Use main agent to implement the plan step by step.
Use
```
tester
```
agent to test the fix and make sure it works, then report back to main agent.
Use
```
code-reviewer
```
subagent to quickly review the code changes and make sure it meets requirements, then report back to main agent.
If there are issues or failed tests, repeat from step 2.
After finishing, respond back to user with a summary of the changes and explain everything briefly, guide user to get started and suggest the next steps.

After fixing, MUST ATTENTION run
/prove-fix
— build code proof traces per change with confidence scores. Never skip.

Closing Reminders

MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using
```
TaskCreate
```
BEFORE starting
MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
MANDATORY IMPORTANT MUST ATTENTION cite
```
file:line
```
evidence for every claim (confidence >80% to act)
MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality
MANDATORY IMPORTANT MUST ATTENTION STOP after 3 failed fix attempts — report outcomes, ask user before #4 MANDATORY IMPORTANT MUST ATTENTION READ the following files before starting:
MANDATORY IMPORTANT MUST ATTENTION search 3+ existing patterns and read code BEFORE any modification. Run graph trace when graph.db exists.
MANDATORY IMPORTANT MUST ATTENTION cite
```
file:line
```
evidence for every claim. Confidence >80% to act, <60% = do NOT recommend.
MANDATORY IMPORTANT MUST ATTENTION include
```
story_points
```
and
```
complexity
```
in plan frontmatter. SP > 8 = split.
MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.