adrianco/retort/update-optimal-blog
Claude Code and agent skill by adrianco/retort
- community
Checking that the SKILL.md file is still in the repository.
Refresh the data tables in optimal-blog.md from master.db. Checks the data for integrity problems FIRST, then runs the generator that picks per-language winners and splices every GEN-marked table, then reconciles the surrounding prose. Use after new experiment results land, or when the optimal-blog numbers are stale.
Install
npx vskill@latest install adrianco/retort/update-optimal-blogSKILL.md
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)
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 in FEATURED_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 to KNOWN_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.py stack_metadata(); if new blanks appear, that path is being bypassed.)
2. Regenerate the tables
This splices every GEN:* block (leading stacks, per-language matrix, per-language winners, prompt method). It only touches text between markers.
3. Verify the round-trip
A second --write MUST produce no further change — the tables are idempotent. If git diff shows table cells changed, that's the real new data; if it shows nothing, the blog was already current.
4. Reconcile the prose
The generator owns the tables, not the sentences around them. After a refresh, read the diff and fix any prose that quotes a number that moved:
- The bullets under Leading stacks (e.g. "Opus 4.8 ~0.59 on hard").
- The per-language recommendation table (the curated one with the prompt/testing column)
and its †/‡ footnotes — reconcile its picks against the generated matrix and winner table. A cell that flipped qualified↔unqualified (e.g. a language gaining local support) changes the recommendation.
- The decision procedure list.
- The language split sentence if a language moved between local and cloud.
You MUST NOT edit numbers inside the GEN markers by hand — re-run the generator instead.
5. Report
Tell the user: which table cells changed, any prose you reconciled, and — if step 1 found anything — what you stopped on. If nothing changed, say the blog was already current.
Constraints Summary
- You MUST run
--healthand clear it against the known gaps before--write. - You MUST NOT hand-edit between
GEN:*markers; the generator is the source of truth. - You MUST NOT introduce or re-elevate a cross-language aggregate as a recommendation —
per-language success rates are the point.
- You MUST verify idempotency (step 3) before considering the update done.
- A new model string or un-ingested experiment is a STOP-and-ask, not a silent skip.
Troubleshooting
`--health` reports an unmapped model — add it to FEATURED_STACKS (with a models list and short column name) if it should appear in the blog, or to KNOWN_NONFEATURED if it's legacy/a serving variant. Re-run health until only the accepted gaps remain.
Blank-model count grew — the harness stopped recording model for some runs. Check stack_metadata() is still called by every runner's provision() (local_runner, metaharness_runner, docker_runner); a new runner or a bypass would reintroduce the original bug. Do not paper over it in the generator.
A local language's number looks too low — confirm the curated selection in FEATURED_STACKS still points at the tuned-config experiments, not an all-experiment average (which includes early bad-config runs and understates the tuned reality).
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/update-optimal-blog/SKILL.md
Popularity
- 7-day trend score
- 5
Not refreshed yet.
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