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 skills add adrianco/retort --skill update-optimal-bloggit clone --depth 1 https://github.com/adrianco/retortWrote 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/adrianco/retort/update-optimal-blog)<a href="https://agentmods.dev/skills/adrianco/retort/update-optimal-blog"><img src="https://agentmods.dev/badge/skills/adrianco/retort/update-optimal-blog/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adrianco/retort/update-optimal-blog"><img src="https://agentmods.dev/badge/skills/adrianco/retort/update-optimal-blog.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00066 | $0.01366 |
| Opus 5 | $0.00033 | $0.00683 |
| Sonnet 5 | $0.00013 | $0.00273 |
| Haiku 4.5 | $0.00007 | $0.00137 |
Grade A, and why
update-optimal-blog 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 9d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Update optimal-blog.md
Overview
optimal-blog.md records what to run today, per language and task size. Its data
tables are not hand-written — they are generated from master.db by
the retort report optimal subcommand (code in
src/retort/reporting/optimal.py) and live between
<!-- GEN:<key> START/END --> markers. This skill is the safe procedure for refreshing
them: check the data before you trust it, regenerate, verify the round-trip, then fix
any prose whose numbers moved.
The order matters. master.db does not record the full stack/config (see the health gaps below), so a blind regenerate can silently publish wrong numbers. Always run the health check first and stop if it reports anything new.
Why per-language, not aggregates
A single cross-language reliability number is misleading — it blends a stack's strong languages with its weak ones (local Qwen passes Python/Go but fails Rust; Opus 4.8 dips on Java). The generator's centrepiece is the per-language success-rate matrix; the leading-stacks routine aggregate is explicitly labelled the least-useful number. Keep it that way — do not "promote" an aggregate back into the recommendation.
Steps
1. Health-check master.db FIRST (gate)
retort report optimal --health
Compare against the known, accepted gaps (already documented in the blog's Keeping this current section):
- ⚠️ No sampling columns /
max_context_tokensunpopulated — the qualified config is curated inFEATURED_STACKS, not filtered from data. - ⚠️ ~250 rows have a blank
model(local provenance bug) — attributed by experiment slug. - ⚠️
experiment-11,experiment-29not ingested.
You MUST STOP and surface to the user if the report shows anything beyond those:
- "Unmapped model strings" — a new model appeared that no featured/legacy entry
covers. Decide whether it's a new featured stack (add to
FEATURED_STACKS) or legacy (add toKNOWN_NONFEATURED) before regenerating. Publishing without deciding would drop it silently. - New experiment dirs not in master.db — results exist on disk but aren't ingested; the refresh would omit them. Re-ingest first, or note the omission to the user.
- A jump in the blank-model count — the harness may have regressed; a run recording no
model is invisible to the tables. (The fix landed in
src/retort/playpen/runner.pystack_metadata(); if new blanks appear, that path is being bypassed.)
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.
- 9d ago First seen · 123 lines · 66 tokens per session scan A 1bd25165e76f
update-optimal-blog is a skill published in the GitHub repository adrianco/retort (203 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 1,366 once invoked, about $0.0003 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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