learning-loop-readout
Skill4 starsChanged 18 days agoMITView on GitHub
Interpret product research, prototype tests, analytics, launch results, or operational signals and update product assumptions and decisions. Use when evidence exists but the team has not yet determined what it changes. Do not use to manufacture certainty from weak or ambiguous signals.
In this repository9 other files it gives its agents.
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
leave the product unchanged because evidence is insufficient.
- Distinctive: line 44
Do not average away severe accessibility, safety, trust, or recovery failures.
- Distinctive: line 46
Never rewrite the canonical context or decision record unless the user authorized file changes.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Learning Loop Readout
Close the loop by turning observations into an explicit product-context and decision update.
Interpret the signal
- Restate the decision and hypothesis that produced the evidence. If none existed, say which conclusions cannot be drawn retrospectively.
- Inventory the observation source, collection method, population, timeframe, sample, missing data, and known bias.
- Separate observations from interpretations. Reconcile qualitative, behavioral, commercial, technical, and operational signals without forcing agreement.
- Determine whether each important assumption is supported, weakened, contradicted, or still unknown. Calibrate confidence to evidence quality.
- Decide what changes:
- update product context;
- retain, revise, reject, or defer a decision;
- change a conceptual model, quality bar, scope, positioning, or implementation;
- leave the product unchanged because evidence is insufficient.
- Record the reason and evidence provenance so another skill can resume without reconstructing the debate.
- Name the next highest-risk unknown and the smallest evidence needed, or state that no further loop is currently justified.
Output
## Decision and prior hypothesis
## Evidence quality
## Observations
## Interpretations and alternatives
## Assumption updates
| Assumption | Prior status | New status | Evidence | Confidence |
## Decision update
- Retain / revise / reject / defer:
- Reason:
## Product context changes
## Next unknown and smallest evidenceBoundaries
- Correlation, engagement, conversion, and retention answer different questions; do not substitute one for another.
- Absence of evidence is not evidence of absence unless the test had power to observe the effect.
- Do not average away severe accessibility, safety, trust, or recovery failures.
- Preserve contradictory evidence and rejected alternatives when they matter to future decisions.
- Never rewrite the canonical context or decision record unless the user authorized file changes.
The loop is closed only when the evidence changes, retains, rejects, or deliberately defers a product decision.
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