| name | lineup-freshness-sweep |
| description | Check the ai-coding-model-guidance knowledge bank for stale entries and produce a prioritized refresh list. Reach for this skill when the knowledge bank's retrieval date is more than 4 weeks old, when a consumer reports a model discrepancy, or on the monthly researcher-reminder cadence. |
Skill: Lineup Freshness Sweep
The cross-tool-model-lineup-2026.md knowledge bank is Tier-4 (fast-churn): model pickers, pricing, and context-window limits change weekly to monthly. A stale lineup produces confidently wrong recommendations. This skill produces a structured refresh checklist that a researcher (or ravenclaude-core/deep-researcher) can execute against primary sources.
When to run this skill
- The knowledge bank's
Last-retrieved header is more than 4 weeks old.
- A developer reports that a model described in the lineup is missing from their picker.
- A developer hits an unexpected charge suggesting a retirement redirect is not documented.
- The
researcher-reminder.yml weekly sweep fires.
Step 1 — Identify all dated claims in the knowledge bank
Open ../../knowledge/cross-tool-model-lineup-2026.md and catalogue every claim that carries a [verify-at-use — YYYY-MM] marker or a Last-retrieved: tag. Group by ecosystem:
GitHub Copilot section:
- Model picker contents (all surfaces × all plans)
- Plan-gated model list (Free sub-list, Pro, Business, Enterprise)
- Org model rules feature availability
OpenAI Codex section:
- Default model id
- Frontier / Pro model id
- Reasoning-level options (flag names, levels available)
- Spark / lightweight tier
xAI Grok section:
- Active model ids (flagship, fast, lightweight)
- Retirement / redirect table
- Any pricing-tier markers
Step 2 — Score each claim by churn risk
| Risk level | Signal | Refresh priority |
|---|
| High | Model ids, picker contents, pricing tiers | Every 2-4 weeks |
| Medium | Plan gates, surface availability, org policy features | Every 4-8 weeks |
| Low | Vendor-neutral methodology, decision-tree logic | Quarterly |
Step 3 — Produce the refresh checklist
For each high-priority claim group, output a research task:
[ ] GitHub Copilot model picker — verify against:
https://docs.github.com/copilot/about-github-copilot/github-copilot-models [verify-at-use]
Check: all surfaces, all plans, any new additions or removals
Last verified: [date from knowledge bank]
[ ] OpenAI Codex default model — verify against:
https://platform.openai.com/docs/guides/code [verify-at-use]
Check: default model id, reasoning-level flag names, any new tiers
Last verified: [date from knowledge bank]
[ ] Grok active lineup + retirement table — verify against:
https://docs.x.ai/docs [verify-at-use]
Check: active model ids, any new retirements or redirects, billing consequences
Last verified: [date from knowledge bank]
Step 4 — Execute or delegate
- If
ravenclaude-core/deep-researcher is available: delegate the checklist; specify primary sources from Step 3; require citation + retrieval date on every updated entry.
- If executing directly: visit each primary source, update the knowledge bank entry with the new value, update the
Last-retrieved: date, and add a [verify-at-use — YYYY-MM] marker with the current month.
- If a claim cannot be verified: mark it
[unverified — training knowledge — reverify YYYY-MM] and flag it prominently in the bank.
Step 5 — Update the bank header
After the sweep, update the bank's top-level Last-retrieved: date and add a one-line sweep note:
Last-retrieved: YYYY-MM-DD
Sweep note: full lineup sweep; [N] claims updated; [M] claims unchanged; retrieval source: [primary URLs]
Pitfalls
- Updating the knowledge bank from memory or training data rather than a live primary-source fetch — this is the failure mode the bank exists to prevent.
- Marking a claim as verified without recording the primary source URL and retrieval date.
- Skipping the retirement table for Grok — silent billing redirects are the highest-blast stale-claim type in this plugin.
- Treating a quarterly sweep as sufficient for model id claims — picker contents can change in days.
See also
