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Run cross-model comparison — scan files with haiku, sonnet, and opus study agents to see how model size affects trigger phrase generation.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Discover files with trigger_phrase and/or refresh frontmatter, let the user choose which to schedule for periodic re-injection via CronCreate. Prevents behavioral drift by keeping core rules and goals fresh in context.
Write trigger phrases from .claude/triggers.json into file frontmatter as model-scoped trigger_phrase fields. Bridges trigger generation (scan) with scheduled injection (zenflow:schedule).
Build a GitHub issue into a PR. User-launched with an issue number — reads the issue, confirms with user, creates branch, implements against acceptance criteria, runs verification, and opens a PR. Every decision surfaces via AskUserQuestion.
Collaborative working session — you and the user are a team exploring, researching, and planning together. When implementation work is needed, create well-structured GitHub issues. The user launches Builder agents to implement and Reviewer agents to verify.
Shed accumulated context mid-session. Writes a knowledge handoff document capturing session state, decisions, observations, and behavioral calibration, then guides the user through /clear and resumption.
Runtime verification using Playwright MCP + proof artifacts. Use when acceptance criteria mention UI behavior, when claiming runtime verification, or when independently verifying a PR's claims. Produces artifact-backed proof — screenshots, DOM snapshots — not prose claims.
Based on SOC occupation classification
| name | compare |
| description | Run cross-model comparison — scan files with haiku, sonnet, and opus study agents to see how model size affects trigger phrase generation. |
Compare trigger phrase generation across model sizes. Same file, same prompt, three models.
*.{ts,tsx,js,jsx,py,go,rs,md,json,yaml,sh,sql,css,scss,html,svelte,vue}), exclude node_modules/, dist/, build/, .git/, *.lock, *.min.*Spawn the compare-researcher agent:
Use Agent tool with:
subagent_type: total-recall:compare-researcher
prompt: |
Compare trigger phrase generation across models for these files:
<list each absolute file path, one per line>
Read .claude/compare-results.json if it exists, otherwise create it. Append a new run entry:
{
"version": 1,
"runs": [
{
"runAt": "<ISO timestamp>",
"files": {
"relative/path.md": {
"models": {
"haiku": { "phrase": "...", "samples": [...], "convergence": [...], "confidence": 0.0 },
"sonnet": { "phrase": "...", "samples": [...], "convergence": [...], "confidence": 0.0 },
"opus": { "phrase": "...", "samples": [...], "convergence": [...], "confidence": 0.0 }
},
"crossModelConvergence": [...],
"modelSpecificTerms": { "haiku": [...], "sonnet": [...], "opus": [...] }
}
}
}
]
}
Use relative paths as keys (relative to project root).
Show a comparison table:
## Cross-Model Comparison Results
| File | Haiku | Sonnet | Opus | Shared Terms |
|------|-------|--------|------|--------------|
| testing.md | test behavior not implementation (0.99) | ... (0.xx) | ... (0.xx) | behavior, implementation |
### Analysis
- Files where all models agree: [list] — these are stable attractors
- Files where models diverge: [list] — larger models may surface deeper associations
- Average confidence: haiku X.XX | sonnet X.XX | opus X.XX
This is experimental data. Production triggers in triggers.json are not affected.