Borrowing it
Nothing to install: this file belongs to radimsem/remindb. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/radimsem/remindb/dev/.claude/skills/add-bench-scenario/SKILL.mdgit clone --depth 1 https://github.com/radimsem/remindbWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/radimsem/remindb/add-bench-scenario)<a href="https://agentmods.dev/skills/radimsem/remindb/add-bench-scenario"><img src="https://agentmods.dev/badge/skills/radimsem/remindb/add-bench-scenario.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 118 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00106 | $0.01710 |
| Opus 5 | $0.00053 | $0.00855 |
| Sonnet 5 | $0.00021 | $0.00342 |
| Haiku 4.5 | $0.00011 | $0.00171 |
Grade A, and why
add-bench-scenario scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add a benchmark scenario
internal/bench/ is remindb's external-facing benchmark — it compares "how many tokens does an agent consume to do X via remindb tools" against "how many would they consume doing X with grep / cat / find". The output is a token-savings table rendered via text/tabwriter. It's invoked from the remindb bench CLI subcommand and exercised end-to-end by scripts/bench-agents.sh.
This is not the Go testing.B benchmark surface — those live as Benchmark* functions inside pkg/*/bench_test.go and have their own discipline.
Where it lands
Two files for a typical scenario, three if it needs CLI flag plumbing.
| File | What changes |
|---|---|
internal/bench/scenarios.go |
New benchXxx(ctx, session, srcDir, ...) (scenarioResult, error) function |
internal/bench/bench.go |
Wire the new scenario into Run, append to results |
cmd/remindb/... (only if new flag needed) |
Surface a new flag on the bench subcommand and pass it into bench.Config |
The scenario function shape
Every scenario implements the same contract: produce one (or more) scenarioResult{name, naiveTok, remindbTok} by measuring two paths to the same answer — the naive path (token count of what an agent would have to read using shell tools) and the remindb path (token count of the tool's response).
Mirror benchTree, benchSearch, or benchFetch in scenarios.go:
func benchExample(ctx context.Context, s *gomcp.ClientSession, srcDir string, budget int) (scenarioResult, error) {
// Naive path: what the agent would read using shell tools.
naive := tokens.Estimate(naiveContent(srcDir))
// remindb path: call the tool and count its response.
text, err := callTool(ctx, s, "MemoryExample", map[string]any{
"budget": budget,
})
if err != nil {
return scenarioResult{}, err
}
return scenarioResult{
name: "example",
naiveTok: naive,
remindbTok: tokens.Estimate(text),
}, nil
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 120 lines · 106 tokens per session scan A fa8a970170a6
add-bench-scenario is a skill published in the GitHub repository radimsem/remindb (125 stars, last pushed 1mo ago), licensed MIT. It adds 106 tokens to every session and 1,710 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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