agentleFS
RiyanKothariRiyanKothari/riyans-claude-skills/skills/verify-before-claiming/SKILL.md

verify-before-claiming

Skill1 starsChanged 38 days agoMITView on GitHub

Discipline for proving work rather than asserting it — running the thing, measuring before optimising, and reporting honestly when results are bad. Use when the user asks whether something works, was tested, or wants honesty; before reporting a score, benchmark or passing suite; before saying a fix is done; whenever a claim is about to outrun its evidence.

3 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    No amount of index tuning would have found that.

    line 33
  2. Very specific:

    Every hook in this project reported "success" across every real session without once running.

    line 54
  3. Distinctive:

    All the manual checks passed — because they ran through PowerShell, not the shell Claude Code actually uses.

    line 57
  4. Distinctive:

    Test the thing the way it really runs, and assert on output that only the real work produces.

    line 60
  5. Distinctive:

    If you find a bug in your own work, lead with it.

    line 68

≈ “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

Verify Before Claiming

Run it, then say it

"It works" without an execution is a guess wearing a confident tone. Before reporting anything as done: run the tests, run the command, load the page.

Build the gate into the tooling so discipline is not required. A scorecard whose correctness score caps at 5/10 without a real test run cannot be talked past. Self-reported flags are the thing to avoid — derive evidence from the run, the transcript, or the filesystem.

Measure before optimising

Guesses about cost are usually wrong, and optimising the wrong thing wastes the effort twice.

A memory hook here looked expensive, so the obvious move was to optimise the index. Measuring first killed that plan:

bare node startup:      166 ms
full memory load + BM25:  ~0 ms   (within noise)

The work was free; the process spawn was the entire cost. The real fix was removing a hook that fired once per tool call — 14.6 spawns per turn down to 2. No amount of index tuning would have found that.

When a number looks bad, check the measurement first

A durability score read 0/10 on a turn that added thirteen tests. The work was fine; nothing was measuring it. A later version used file mtimes and inflated the same score by counting the previous turn's files.

Check whether the measurement is broken before assuming the work was.

Validate against reality, not intuition

A prompt classifier felt accurate. Backtested against 117 real transcript turns it scored 26.5%, and the confusion matrix showed it labelling almost everything "moderate". Two data-driven fixes took it to 43.6%.

Where past runs are recorded — transcripts, logs, CI history — that is a free labelled dataset. Use it instead of trusting the feeling.

An exit code is not evidence

Every hook in this project reported "success" across every real session without once running. The command was cmd /c "..."; under Git Bash, /c is rewritten to C:/, so cmd opened interactively, read the payload as input and exited 0. All the manual checks passed — because they ran through PowerShell, not the shell Claude Code actually uses.

Test the thing the way it really runs, and assert on output that only the real work produces. "It exited cleanly" and "it worked" are different claims.

Report honestly

State the number even when it is bad, name the weakest part, and say why. A low score that names its cause is worth more than a high one that hides it.

If you find a bug in your own work, lead with it. Shipping a known defect quietly costs far more than the embarrassment of naming it.

Your own tests are not automatically right

Several failures here were wrong expectations, not wrong code. When a test fails, decide which is wrong before touching either — and never weaken an assertion just to get green.

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