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Outcome

Skill brian8a1/senso-agent-skills/skills/outcome

Review and record the observed Outcome of a Done Senso Solution after launch. Use when a product manager, designer, or engineer has post-launch metrics, research, incidents, customer evidence, or an observation window and needs to compare the result with the original expectation as success, partial, miss, or unexpected.From its SKILL.md

Install
npx -y skills add brian8a1/senso-agent-skills --skill outcome

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.0 KB, 332 tokens by cl100k_base, as published. Nobody here has run it

Record post-launch learning

Close the loop only with observed evidence. Output such as a design, PR, merge, or deployment is not an Outcome.

Procedure

  1. Resolve the Solution with senso_lookup and verify that it is Done/Launched. Read its Opportunity, original expectation, Decision history, and relevant Living Document.
  2. Establish the baseline, post-launch value or qualitative observation, measurement window, affected segment, source, and confounders.
  3. Use senso_analyze when Senso contains the necessary multi-entity or time-window data. If a supplied external metric or research result is not yet in Senso, capture it with senso_capture before recording the Outcome.
  4. Compare evidence with the original expectation and propose one classification: success, partial, miss, or unexpected.
  5. Stop when the window is incomplete or evidence is insufficient. State exactly what and when to measure next.
  6. After human confirmation, call senso_decide with mode: record_outcome, the before/after values when available, and concise learning notes.
  7. Report the stored Outcome and any reopened Opportunity or follow-up Decision indicated by newer Signals.

Guardrails

  • Do not infer impact from completion, launch, adoption anecdotes, or the absence of complaints.
  • Preserve contradictory evidence and confounders.
  • Do not rewrite the historical Solution to make the Outcome look better.
  • Create a follow-up Solution only through a separate Opportunity Decision.

Result

Return the observed evidence, comparison window, confirmed classification, stored Outcome reference, and next learning question.

What ships with it: 1 file

481 B alongside SKILL.md

agents/

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