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/verification-strategy

Thorough verification of completed work before declaring done

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pydantic-deepagents
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$ npx -y skills add vstorm-co/pydantic-deepagents --skill verification-strategy --agent claude-code

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How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/verification-strategy

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Thorough verification of completed work before declaring done

SKILL.md

verification-strategy.SKILL.md
name: verification-strategy
description: "Thorough verification of completed work before declaring done"
tags: [testing, verification, benchmark]
version: "1.0.0"

Verification Strategy

How to verify your work is correct before declaring a task complete.

The Verification Checklist

After implementing a solution, go through ALL of these:

1. Re-read the original task

  • Re-read the EXACT wording of the task instruction
  • Check every requirement — file paths, names, formats, constraints
  • Are there implicit requirements? ("compile with gcc -O3 -lm" means exact flags)

2. Check file outputs

  • Do all required files exist at the exact paths specified?
  • Are file sizes within any stated limits?
  • Is the file format correct? (binary vs text, encoding, line endings)
ls -la /path/to/expected/output
wc -c /path/to/output    # byte count
file /path/to/output      # format detection
head -5 /path/to/output   # content preview

3. Compile and run

  • Compile with the EXACT flags specified in the task
  • Run with the EXACT command and arguments specified
  • Check exit code: `echo $?` (should be 0 for success)
  • Check both stdout AND stderr

4. Validate output

  • Compare output against expected format
  • Check exact field names, delimiters, number formatting
  • If the task specifies output format, match it exactly:
  • JSON: valid JSON? correct schema?
  • CSV: correct headers? correct delimiter?
  • Plain text: correct line endings? trailing newline?

5. Test edge cases

  • Empty input (if applicable)
  • The specific test inputs mentioned in the task
  • Large inputs (if the task involves performance)

6. Check constraints

  • Size limits (file size, code length)
  • Time limits (does it finish in reasonable time?)
  • Memory limits (does it stay within bounds?)
  • No external dependencies that aren't available

Reading Test Scripts

If you can find the test script, READ IT:

  • What exact assertions does it make?
  • What inputs does it use?
  • What output format does it expect?
  • What timeouts are set?

Tests often check things you didn't expect:

  • Exact string matching (whitespace matters!)
  • Specific numeric precision
  • File permissions
  • Process exit codes

Common Verification Failures

| What you think is right | What the test actually checks | |------------------------|-------------------------------| | Output on stdout | Output in a specific file | | Human-readable numbers | Exact decimal precision | | Relative file paths | Absolute file paths | | "Close enough" answer | Exact match | | Code that runs | Code under a specific size limit |

Final Check

Before declaring done, ask yourself: 1. Did I create/modify ALL the files the task asks for? 2. Did I use the EXACT paths, names, and formats specified? 3. Does my code compile/run WITHOUT errors? 4. Does my output match what automated tests would expect? 5. Have I cleaned up any debug output or temporary files?

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Repo: vstorm-co/pydantic-deepagents