skill-usage-report
Skill8 starsChanged 4 months agoMITView on GitHub
Analyzes Claude Code Skill invocation history via cc-skill-usage: leaderboards, per-skill drilldowns, project and time filters, triggering context, and a mandatory cross-verification step whenever a count looks surprising.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- sonichi/sutandotheir own version, first seen May 17, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- sonichi/sutando★ 396same 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.
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
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 36
If a count is unexpectedly low, unexpectedly high, or contradicts the user's own memory of how much they used something, do not report it …
- Very specific: line 50
If the independent pipeline matches cc-skill-usage's own count, the tool is right and the surprise is the invocation-vs-mention gap …
≈ “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
Skill Usage Report
Runs and interprets cc-skill-usage (this repo's own CLI) to answer
questions about Skill usage. Read README.md and EXAMPLES.md in this repo
first if either is unfamiliar, they hold the full flag reference and the
scenario-to-command mapping this skill draws from.
Picking the right command
Match the question to the scenario in EXAMPLES.md rather than improvising
flags: a monthly recap is --since 30d --show-context, a single skill's
health check is <skill-name> --show-context, a repo-versus-everything-else
comparison needs two --json runs diffed by skill name, and so on. Default
to --show-context whenever the user's question includes "why", "when", or
"in what context": counts alone rarely answer that.
The one non-negotiable step: verify before reporting a surprising number
If a count is unexpectedly low, unexpectedly high, or contradicts the user's own memory of how much they used something, do not report it as-is. Cross- check it independently before presenting a verdict:
# raw mentions (expect this far higher, it includes prose, files, summaries)
grep -rl 'skill-name' ~/.claude/projects --include="*.jsonl" | wc -l
# real invocations, independent of cc-skill-usage's own parsing code
grep -rh '"name":"Skill"' ~/.claude/projects --include="*.jsonl" \
| jq -r '.message.content[]? | select(.type=="tool_use" and .name=="Skill") | .input.skill' \
| grep -c '^skill-name$'If the independent pipeline matches cc-skill-usage's own count, the tool is
right and the surprise is the invocation-vs-mention gap (measured at 212x on
real data during this tool's own development, see README.md), not a bug.
State that plainly, with both numbers, rather than either blindly trusting
or blindly doubting the first result.
Known blind spots to disclose, not paper over
State these when relevant instead of letting a report imply more precision than the data supports:
- Typed slash-command invocations may be under-represented versus
natural-language-triggered ones; the invocation signature this tool
matches is a
Skilltool call, and that path is well verified, but a purely-typed/skill-namepath has fewer confirmed clean examples. --show-contextoccasionally reports "(no preceding user message found)" when the real preceding message was a large tool result, skipped by design to avoid an OOM failure mode. That gap affects the context line only, never the invocation count itself.- Scanning a live session's own transcript while investigating a skill can make raw text-mention counts (never real invocation counts) pick up an echo of your own prior grep output.
Output shape
Lead with the verdict (the number, and whether it was cross-checked), then the supporting breakdown (by project, by day, sample context), matching the density expected for a factual/debug answer: direct, no hedging once verified, but never state a surprising number as fact without having run the verification step above.
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.

