Front-end diagnostic framework

SkillDev tools

Use when diagnosing frontend build failures, runtime errors, UI anomalies, API/data problems, white screens, request failures, or unexplained production exceptions; Chinese triggers include debugging, debug, troubleshooting, positioning, error reporting, exceptions, white screens, request failures.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Front-end diagnostic framework skill

What this skill tells your AI

The instructions your AI receives, as published by bovinphang/frontend-craft in skills/fec-debug-framework/SKILL.md and read by ahel’s review.

Purpose

Use an evidence-driven triage, collection, hypothesis, verification, and remediation process to locate front-end faults and avoid relying on intuition to expand the scope of changes.

Procedure

All front-end problem diagnosis follows a unified process:

Step 1: Classify

Identify problem type and scope of impact:

TypeJudgment basisDiagnosis entrance
buildCommand exit is non-zero, stderr has error→ Build module
runtimeConsole exception, white screen, function unavailable→ Runtime module
uiVisual deviation, interaction not as expected→ UI module
apiRequest status code exception, data inconsistency→ API module

Cross-type problems (such as API failure leading to UI exceptions) start with the most superficial symptoms and drill down layer by layer.

Step 2: Collect

Collect evidence by type (specific strategies for each module, see below).

Step 3: Hypothesize

Propose possible root causes based on evidence, ranked by likelihood:

  • Every hypothesis must be testable (have clear verification methods)
  • Keep at most 3 hypotheses to avoid divergence
  • Format: "Because X leads to Y, which can be verified by Z"

Step 4: Verify

Test the hypotheses one by one:

  • Start with the most likely hypothesis
  • Only change one variable at a time
  • Verification result record: confirmed / falsified / pending
  • If all assumptions are falsified, return to Step 2 and collect again.

Step 5: Fix & Validate

  • Apply minimal fixes
  • Run affected verification commands
  • Confirmed no regression
  • Output repair report

Diagnostic module

Build module

Collect: Run minimal failing commands, capture full stderr/stdout Assumptions: Grouped by error type (type error, import failure, configuration resolution, missing dependency), match known patterns Verification: Fix a type of root cause → rerun the command → confirm that errors are reduced Special handling:

  • Collect evidence for dependency version, peer dependency, ESM/CJS, and lockfile related failures first as build compatibility issues
  • Log package manager, Node version, lockfile diff, related package version and full error log
  • No longer upgrade dependencies, manually edit lockfiles when evidence is lacking, or mix dependency migrations with normal debug fixes in the same batch of changes
  • If the task goal itself is version upgrade, CVE repair or lockfile risk review, it should be transferred to the dependency upgrade workflow
  • CI exclusive failure check Node version, package manager, environment variable differences

Runtime module

Collect:

  • Recurrence path (sequence of user operations)
  • Console errors and stack
  • Component rendering tree status (check whether key components are mounted correctly)
  • Relevant store/state snapshot

Assumptions:

  • Stack reverse tracing: trace back from the exception location to the trigger source
  • State flow analysis: Check whether state changes are as expected
  • Life cycle analysis: whether uninitialized data is accessed at the wrong time

Verification:

  • Add temporary log confirmation status value
  • Add assertions on suspicious paths
  • Recurrence path verification fix

UI module

Collect:

  • Current screenshot vs desired effect
  • DOM structure check (whether the element exists and whether the level is correct)
  • Computed style checks (actual applied CSS values)
  • Responsive breakpoint testing

Assumptions:

  • CSS specificity conflict (selector weight is not enough to be overridden)
  • Component state mismatch (props/state not passed correctly)
  • Layout model problem (flex/grid configuration error)
  • Missing responsive breakpoints

Verification:

  • Browser DevTools real-time adjustment verification
  • Isolated component testing (excluding external style interference)
  • Multiple breakpoints to verify one by one

API module

Collect:

  • Request URL, method, headers, body
  • Respond to status, headers, body
  • Network waterfall timing
  • Cache data in related store/state

Assumptions:

  • Request link hop-by-hop inspection (URL → Middleware → Interceptor → Server)
  • Data conversion checks (response parsing, type mapping)
  • Cache policy check (expiration, invalidation, race condition)
  • Concurrent request race condition (race condition)

Verification:

  • curl independent reproduction (excluding front-end interference)
  • Mock layer by layer to locate the problem level
  • End-to-end request validation fixes

Detailed reference

When writing a diagnostic report, load references/report-template.md.

Constraints

  • Don't guess at root cause when evidence is lacking
  • Not "fixed" by turning off rules, removing tests, or reducing type safety
  • Change only one variable at a time to test your hypothesis
  • Do not expand the scope of changes before verification
  • The same hypothesis fails verification 3 times in a row, stops and reports blocking

Expected Output

  • The diagnostic report is saved as reports/debug-YYYY-MM-DD-HHmmss.md
  • The report includes issue type, key evidence, hypothesis verification records, root cause, fix content, verification results and remaining risk
  • Build/runtime/ui/api problems can explain the reproduction path, verification command or next blocking point

Signals

GitHub stars
21
Forks
3
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
fec-debug-framework
Source
github.com/bovinphang/frontend-craft