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.
Adopted by
Most recently taken up by
- Nashova/LLM-Dev-Frameworktheir own version, first seen Nov 1, 2026
- songjiun10-collab/Senior-thinking-skillstheir own version, first seen Oct 12, 2026
- LeonardLeroy/i-dont-believe-youtheir own version, first seen Sep 24, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- songjiun10-collab/Senior-thinking-skills★ 87same name
- LeonardLeroy/i-dont-believe-you★ 18same name
- Nashova/LLM-Dev-Framework★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository6 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- debug-systematicallySkill
- ralph-loopSkill
- safe-operationsSkill
- secrets-hygieneSkill
- token-harnessSkill
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
- Distinctive: line 33
No amount of index tuning would have found that.
- Very specific: line 54
Every hook in this project reported "success" across every real session without once running.
- Distinctive: line 57
All the manual checks passed — because they ran through PowerShell, not the shell Claude Code actually uses.
- Distinctive: line 60
Test the thing the way it really runs, and assert on output that only the real work produces.
- Distinctive: line 68
If you find a bug in your own work, lead with it.
≈ “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.
Files
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.
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
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.

