agentleFS
david-vrbadavid-vrba/claude-power-skills/skills/double-check/SKILL.md

double-check

Skill4 starsChanged 5 months agoMITView on GitHub

Rigorously review the work for defects, fix what's safely fixable, verify, and end with a confidence rating out of 10. Hunts careless slips, code-quality issues, logic gaps, intent-vs-execution mismatches, and edge cases. An audit-and-fix pass over existing work, not a fresh build.

10 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 10 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Default: the work just done — the current changes (git diff if available) and the files touched this session.

    line 11
  2. Distinctive:

    After everything, output exactly this — no lead-in sentence:

    line 28
  3. Distinctive:

    If below 10, add one short line naming the single biggest remaining risk.

    line 32

“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

double-check

Scrutinize the work for anything wrong — silly slips, bad code, logic gaps, things that don't make sense, edge cases — then fix what's safely fixable, verify it, and rate your confidence out of 10. This is an active audit: investigate → plan → fix → verify, not just a list of complaints.

Scope

Default: the work just done — the current changes (git diff if available) and the files touched this session. If the user names a target (file, folder, feature), scope to that. Investigate the actual code; never assume it's fine because it "looks" right.

The five review lenses

  1. Careless errors (correctness slips) — wrong file paths, incorrect folder/directory references, typos, wrong variable/function names, copy-paste leftovers, off-by-one, mismatched identifiers, stale comments. The small stuff that silently breaks things.
  2. Code quality / maintainability — hold a clean-code bar: one function = one responsibility (split it if it does two things), intention-revealing names, no boolean flag arguments (a function that switches behavior on true/false is two functions in disguise), the simplest thing that works, no dead code, a 1–2 line header on each file. Flag duplication and needless complexity.
  3. Logic gaps (unhandled conditions) — the divide-by-zero class: missing guards, absent error handling, null/empty/missing inputs, failure paths real users will hit. These map to concrete ways the program falls over in real use.
  4. Coherence (intent vs. execution) — does what was built actually achieve the stated goal? Catch the cases where the implementation does something different from what was intended, or solves the wrong problem. More general than #3 — can be about the idea or the approach, not just the code.
  5. Edge cases & boundaries — enumerate all plausible ones: empty/null, min/max limits, concurrency/races, latency/timeouts, very large input, unicode/encoding, offline/permission failures, unexpected order. Some only surface with real testing or data — predict them anyway.

Procedure — do the work, don't just report it

  1. Review — read the real code/files across all five lenses; gather findings.
  2. Plan — rank findings by severity (breaks things · should fix · minor). Decide which fixes are safe and easy to apply now vs. which to only flag (risky, large, or needing real test data).
  3. Fix — apply the safe, clear fixes. For edge cases: fix the cheap/safe ones; list the rest with why they're deferred. Don't make risky or large changes without surfacing them first.
  4. Verify — actually confirm each fix: run it, run tests, or trace the logic. Make sure nothing regressed. State what you verified and how.
  5. Report — concise: what was found, what you fixed, what's still open (and why). Point to locations (file:line), don't dump full files.

Always end with the confidence rating (last line, no preamble)

After everything, output exactly this — no lead-in sentence:

Confidence: N/10 — code works as intended, flaw-free, across edge cases.

If below 10, add one short line naming the single biggest remaining risk. Nothing more.

Rough anchor: 10 = exhaustively verified, nothing left; 7–9 = solid, only minor/unlikely edges unverified; 4–6 = happy path works, real gaps remain; 1–3 = likely broken or unverified.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.