agentleFS
motola10motola10/claude-code-base/.claude/skills-disabled/agent-code-to-confirm/SKILL.md

agent-code-to-confirm

Skill1 starsChanged 5 months agoMITView on GitHub

Implementation confidence gate for code — before writing or after. When triggered, the agent enters a mode of brutal honesty: no assumptions, no guessing, no proceeding below 95% confidence. Pre-code: audits its understanding, reads the actual codebase, surfaces every unknown, and locks in a test plan before touching a file. Post-code: runs or traces the implementation, checks regressions in affected paths, removes temporary verification scripts. Trigger on: "confirm before you code", "make sure before you code", "be sure about this", "check before implementing", "verify before you start", "make sure this is right", "confirm this works", "double-check the implementation", "are you sure about this approach", "be careful on your implementation", "confirm first your solution", "validate before you proceed", or any phrase signalling the user wants the agent to be sure before or after coding.

The file

Show its rules and text

We split this skill into the rules it gives an agent

14 rules. About 0 are common, 14 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈14

    Distinctive · estimate

    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:

    When triggered, enter a high-integrity implementation mode.

    line 18
  2. Distinctive:

    If confidence in the approach or the result is below 95%, stop and surface the gaps — don't push through

    line 21
  3. Distinctive:

    Remove any temporary scripts or code written only to probe or verify behavior — they're not artifacts

    line 23
  4. Distinctive:

    Before doing anything else, establish which situation applies:

    line 31
  5. Distinctive:

    If it's unclear, say so and ask one question to resolve it.

    line 37
  6. Distinctive:

    4. Commit to a verification plan: If confidence ≥ 95%, write down how the implementation will be verified once it exists — happy path, at …

    line 58
  7. Distinctive:

    1. Read the implementation: Read every changed file. Trace the execution path with a concrete example — inputs, expected outputs, actual …

    line 91
  8. Distinctive:

    Run existing tests that cover the changed area

    line 94
  9. Distinctive:

    Write a focused test or script that exercises the implementation, then remove it after (if it's worth keeping, move it to the test suite …

    line 95
  10. Distinctive:

    Don't inflate confidence to clear the 95% bar — the bar only has value if you respect it

    line 129
  11. Distinctive:

    Don't label something Verified if you read about it rather than read it directly

    line 130
  12. Distinctive:

    Don't hedge with generic caveats — name the specific gap or don't mention it

    line 131
  13. Distinctive:

    Don't leave temporary probe scripts in the repo — remove them when done

    line 132
  14. Distinctive:

    Don't ask the user for something you can look up yourself right now

    line 133

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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

What this skill does

When triggered, enter a high-integrity implementation mode. The rules are simple:

  • Make no assumptions you haven't verified by reading actual files or running actual commands
  • If confidence in the approach or the result is below 95%, stop and surface the gaps — don't push through
  • Surface uncertainty as a concrete gap, not a vague caveat
  • Remove any temporary scripts or code written only to probe or verify behavior — they're not artifacts

This applies whether the code exists yet or not. The mode adjusts based on context.


Determine the mode

Before doing anything else, establish which situation applies:

Plan mode — the implementation doesn't exist yet. The task is to verify the approach before writing code.

Verify mode — the implementation already exists. The task is to confirm it works and didn't break anything.

If it's unclear, say so and ask one question to resolve it. Don't guess.


Plan mode

The goal is to earn the right to write the code by proving you understand what you're about to do.

1. Read before reasoning. Identify every file, function, and interface the implementation will touch or depend on. Read them. Don't describe what you think the codebase looks like — confirm it.

2. Label every key component:

LabelMeaning
VerifiedDirectly confirmed by reading a file or running a command
InferredFollows logically from verified facts
AssumedBased on convention or pattern, not confirmed for this context
UncertainUnknown — would be guessing

3. Assess confidence. Confidence is bounded by the weakest critical component. If any critical item is Uncertain, confidence cannot reach 95%. Multiple Assumed items in the critical path are also a red flag. Ask: "if any of these turn out to be wrong, does the implementation fail or silently misbehave?"

4. Commit to a verification plan. If confidence ≥ 95%, write down how the implementation will be verified once it exists — happy path, at least one edge case, and any adjacent behavior worth spot-checking. This is a promise, not a formality.

Respond:

**Ready to implement**

Verified: [what was directly confirmed]
Inferred: [what follows from that]
Assumed: [anything not directly confirmed, and why it's acceptable to proceed]

Verification plan:
- [check 1]
- [check 2]

Or if confidence is below 95%:

**Not ready — confidence: [X]%**

Confident about: [what you know]
Gaps: [specific unknowns — not vague]

Need to resolve before proceeding: [1–2 focused questions]

Verify mode

The goal is to confirm the implementation actually does what it claims — not just that it compiles or looks right.

1. Read the implementation. Read every changed file. Trace the execution path with a concrete example — inputs, expected outputs, actual behavior.

2. Run or trace verification steps. At minimum one of:

  • Run existing tests that cover the changed area
  • Write a focused test or script that exercises the implementation, then remove it after (if it's worth keeping, move it to the test suite instead)
  • Trace through the logic manually with a real example

3. Check for regressions — only if existing code was modified. If you changed existing behavior (not just added new code), verify that callers and adjacent paths still work as before. If you only added new code with no connections to existing behavior, regression checking is not required — say so explicitly.

4. Apply the same labeling. Verified, Inferred, Assumed, Uncertain. Same confidence threshold.

Respond:

**Implementation verified**

Verified: [what was directly confirmed — tests run, traces done, outputs matched]
Inferred: [what follows from that]
Regressions: [checked and clear / not applicable — reason]

Cleaned up: [temporary scripts removed / none created]

Or if confidence is below 95%:

**Not verified — confidence: [X]%**

Confirmed: [what checks passed]
Gaps: [specific things that couldn't be confirmed]

To reach 95%: [1–3 focused next steps]

What not to do

  • Don't inflate confidence to clear the 95% bar — the bar only has value if you respect it
  • Don't label something Verified if you read about it rather than read it directly
  • Don't hedge with generic caveats — name the specific gap or don't mention it
  • Don't leave temporary probe scripts in the repo — remove them when done
  • Don't ask the user for something you can look up yourself right now

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.