Skill

/calibrate

Feed outcome data back into the model. Updates quality weights, engagement parameters, and optionally skill/agent prompts.

Install

npx claudepluginhub jobrien127/plugin-marketplace --plugin solution-engine

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Feed outcome data back into the model. Updates quality weights, engagement parameters, and optionally skill/agent prompts.

SKILL.md

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Last CommitApr 24, 2026

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/calibrate

Feed outcome data back into the model. Updates quality weights, engagement parameters, and optionally skill/agent prompts.

Pipeline Position

Requires: collect complete (always available once pipeline started)

Produces: updated math module parameters, weight-history.jsonl append

Does not advance stages — calibrate is a continuous loop, not a gate

Instructions

Determine target: math (default), experience, or framework.

Load feedback events from $PROBLEMS_ROOT/$ARGUMENTS/feedback/events/.

Load current weight history from feedback/weight-history.jsonl.

--target math (default)

Load $MATH_ROOT/optimization/05-quality-score.md for current weights.

Load ${CLAUDE_PLUGIN_ROOT}/templates/FEEDBACK_TEMPLATE.md for ladder structure.

Process L0 events (post-match ratings) → short loop: Bayesian update on w_k.

Process L1 events (7-day rebooking) → medium loop: engagement model update.

Process L2 events (30-day active) → long loop: flag for human review if thresholds shift.

Check ladder correlations. If any break → trigger ladder break protocol.

Append to weight-history.jsonl.

--target experience

Analyze feedback events for experience layer signals (cancellation rates, nudge response, etc.)

Suggest updates to experience specs based on observed patterns.

--target framework

Evaluate pipeline output quality (did specs make sense? did scaffold build?)

Suggest updates to skill/agent prompts based on what worked vs. didn't.

Write suggestions — don't auto-modify skills without human approval.

Update pipeline-state.json calibrate timestamp.

/calibrate

Feed outcome data back into the model. Updates quality weights, engagement parameters, and optionally skill/agent prompts.

Pipeline Position

Requires: collect complete (always available once pipeline started)
Produces: updated math module parameters, weight-history.jsonl append
Does not advance stages — calibrate is a continuous loop, not a gate

Instructions

Determine target: math (default), experience, or framework.
Load feedback events from $PROBLEMS_ROOT/$ARGUMENTS/feedback/events/.
Load current weight history from feedback/weight-history.jsonl.

--target math (default)

Load $MATH_ROOT/optimization/05-quality-score.md for current weights.
Load ${CLAUDE_PLUGIN_ROOT}/templates/FEEDBACK_TEMPLATE.md for ladder structure.
Process L0 events (post-match ratings) → short loop: Bayesian update on w_k.
Process L1 events (7-day rebooking) → medium loop: engagement model update.
Process L2 events (30-day active) → long loop: flag for human review if thresholds shift.
Check ladder correlations. If any break → trigger ladder break protocol.
Append to weight-history.jsonl.

--target experience

Analyze feedback events for experience layer signals (cancellation rates, nudge response, etc.)
Suggest updates to experience specs based on observed patterns.

--target framework

Evaluate pipeline output quality (did specs make sense? did scaffold build?)
Suggest updates to skill/agent prompts based on what worked vs. didn't.
Write suggestions — don't auto-modify skills without human approval.

Update pipeline-state.json calibrate timestamp.