smithery/ryanthedev

aposd-verifying-correctness

Verifies implementation completeness across functional correctness, error handling, concurrency, and security dimensions using APOSD's post-implementation checklist. Run after a coding task is nominally complete, not during active bug investigation (use cc-debugging).

Installation

$ npx skills add smithery/ryanthedev --skill aposd-verifying-correctness

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,523 B
  • docs SUMMARY.md 447 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Skill: aposd-verifying-correctness

Before "Done" — scope check

Design quality ≠ correctness. Well-designed code can still have bugs, missing requirements, or safety issues.

Run every dimension check before claiming done — "I think I covered everything" without explicit mapping is exactly the gap this skill exists to close.


Dimension Detection & Checks

For each dimension: detect if it applies, then verify.


1. Requirements Coverage

Detect: Were requirements stated? (explicit list, user request, spec)

If YES, verify:

  • List each requirement explicitly
  • For each: point to code that implements it
  • Any requirement without code? → Not done
  • Any code without requirement? → Scope creep or missing requirement

Red flag: "I think I covered everything" without explicit mapping


2. Concurrency Safety

Detect: Any of these present?

  • Multiple threads/processes accessing same data
  • Async/await patterns
  • Shared mutable state (class attributes, globals)
  • "Thread-safe" in requirements or docstring
  • Web handlers, queue workers, background tasks

If YES, verify:

  • All shared mutable state identified
  • Each access point protected (lock, atomic, queue, immutable)
  • No time-of-check to time-of-use (TOCTOU) gaps
  • Lock ordering consistent (if multiple locks)

Red flag: "It's probably fine" or "Python GIL handles it"


3. Error Handling

Detect: Can any operation fail?

  • I/O (file, network, database)
  • External calls (APIs, subprocesses)
  • Resource acquisition (memory, connections)
  • User input processing
  • Parsing/deserialization

If YES, verify:

  • Each failure point has explicit handling OR propagates
  • No bare except: or except Exception: pass
  • Error messages actionable (what failed, why, how to fix)
  • Partial failures handled (rollback, cleanup, consistent state)
  • No error path silently continues as if nothing happened (catch-log-continue, default returns on failure, swallowed callbacks all create silent failures — each error path either surfaces to the caller or is observable via logs/metrics/alerts)

Red flag: "Errors are rare" or "caller handles it" without checking caller


4. Resource Management

Detect: Does code acquire resources?

  • File handles, sockets, connections
  • Locks, semaphores
  • Memory allocations (large buffers, caches)
  • External service handles
  • Background threads/processes

If YES, verify:

  • Every acquire has corresponding release
  • Release happens in finally/context manager/destructor
  • Release happens on error paths too
  • No resource leaks on repeated calls
  • Bounded growth (caches have limits, queues have limits)

Red flag: "It cleans up eventually" or daemon threads without shutdown


5. Boundary Conditions

Detect: Does code handle variable-size input?

  • Collections (lists, dicts, sets)
  • Strings, byte arrays
  • Numeric ranges
  • Optional/nullable values

If YES, verify:

  • Empty input: What happens with [], "", None, 0?
  • Single item: Edge case often different from N items
  • Maximum size: What if input is huge? Memory? Time?
  • Invalid values: Negative numbers, NaN, special characters?
  • Type boundaries: int overflow, float precision?

Red flag: "Nobody would pass that" or "that's an edge case"


6. Security (if applicable)

Detect: Does code handle untrusted input?

  • User-provided data (forms, API requests)
  • File contents from external sources
  • URLs, paths, identifiers from users
  • Data that becomes SQL, shell, HTML, or code

If YES, verify:

  • Input validated before use
  • No string concatenation for SQL/shell/HTML (use parameterized)
  • Path traversal prevented (no ../ exploitation)
  • Secrets not logged or exposed in errors
  • Auth/authz checked before action, not after

Red flag: "It's internal only" (internals get exposed)


Detailed per-dimension checklists: Read(${CLAUDESKILLDIR}/checklists.md)


Output Format

When verifying, output:

## Correctness Verification

### Requirements: [PASS/FAIL/N/A]
- Requirement 1 → implemented in X
- Requirement 2 → implemented in Y

### Concurrency: [PASS/FAIL/N/A]
- Shared state: [list]
- Protection: [how]

### Errors: [PASS/FAIL/N/A]
- Failure points: [list]
- Handling: [approach]

### Resources: [PASS/FAIL/N/A]
- Acquired: [list]
- Released: [how]

### Boundaries: [PASS/FAIL/N/A]
- Edge cases: [list]
- Handling: [approach]

### Security: [PASS/FAIL/N/A]
- Untrusted input: [list]
- Validation: [approach]

**Verdict:** [DONE / NOT DONE - list blockers]

Relationship to Other Skills

Skill Focus When
aposd-designing-deep-modules Design quality FIRST—during design
aposd-verifying-correctness Actual correctness BEFORE "done"
cc-quality-practices Testing/debugging Throughout

Order: Design → Implement → Verify (this skill) → Done


Chain

After Next
All dimensions pass Done (pre-commit gate)