Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/gideondk/strata/evalnpx skills add gideondk/strata --skill evalgit clone --depth 1 https://github.com/gideondk/strataWhat 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 | $0.00085 | $0.00465 |
| Opus 5 | $0.00043 | $0.00233 |
| Sonnet 5 | $0.00017 | $0.00093 |
| Haiku 4.5 | $0.00009 | $0.00047 |
Grade A, and why
strata:eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
strata:eval
A regression guard + measuring stick for vault recall. It runs a small,
committed set of query → expected notes cases through the same retrieval the
recall tool uses, and reports recall@k and MRR.
The golden set
Lives at <vault>/<repo>/.eval/golden.json (commit it — it's versioned with
the vault):
{
"cases": [
{"query": "rate limiting policy",
"expected": ["decisions/2026-05-21-token-bucket.md"],
"scope": "decisions"}
]
}
scope is optional (null/omitted = all scopes). 20–50 hand-picked cases is
plenty. Seed them from queries you actually run, or from the usage ledger's
top-recalled notes.
Run it
"${CLAUDE_PLUGIN_ROOT}/bin/strata" eval -k 5
Measure the rerank lift
Compare the pipeline with and without the cross-encoder rerank:
"${CLAUDE_PLUGIN_ROOT}/bin/strata" eval -k 5 --sweep
--sweep runs the golden set rerank-OFF then rerank-ON and prints both rows +
the lift, so the decision is a number. If the lift is zero (or negative) on your
set, leave rerank off (it's off by default) — don't pay the per-call model load
for no gain.
When to run
- Before/after any retrieval change (rerank, RRF weighting, a new scope).
- Periodically, as a regression guard — a drop means recall quality slipped.
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.
- 2d ago First seen · 55 lines · 85 tokens per session scan A d69bcde2a135
strata:eval is a skill published in the GitHub repository gideondk/strata (5 stars, last pushed 1mo ago), licensed MPL-2.0. It adds 85 tokens to every session and 465 once invoked, about $0.0004 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 2026-08-31.
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