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by Boboshu2/agentops446 stars
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Define intended behavior, review write scope and assess reversible decisions. Use when: discovery needs clarification or resumption before one complete slice; stop once actionable.

Use this Skill: https://skilld.dev/gh/boshu2/agentops/plan

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referencesground-truth-routing.md

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Optional probes and prototypes

Use this method only when an observation could change a consequential approach or clarify the caller's reaction to concrete behavior. First inspect existing evidence: a clear task or already answered question needs no prototype, stock quickstart, control experiment or deviation ledger.

Before running anything, state in the existing intent or handoff:

  1. The question and assumption under test, including the accepted behavior that must remain true.
  2. The discriminator: competing predictions and the observation that would distinguish them. A mock-up may elicit a caller preference; it cannot prove runtime behavior, durability or integration that it does not exercise.
  3. The disposable scope, authorized inputs/destination and real time or cost bound. Use the smallest safe construction; do not touch production or widen write authority to make a prototype realistic.
  4. The stop: sufficient distinguishing evidence, an inaccessible prerequisite, no useful new observation, or the existing resource limit. A stopped or inconclusive probe leaves the assumption unresolved; it does not justify retrying until the preferred answer appears.

Choose relevant ground truth, not a compulsory sequence:

Question Useful evidence or discriminator
Does an external substrate already provide the needed behavior? Current vendor documentation and pinned stock behavior; run a vanilla quickstart only when it answers the named uncertainty. Compare proposed additions with native capabilities before rebuilding them.
Can the repository's existing approach satisfy the example? Trace its relevant behavior and try the simplest acceptance-relevant change or test. Retain evidence if it cannot meet the example.
Can a new path work end to end? A walking skeleton through the uncertain boundary, with an observable result.
Which interaction does the caller want? A small concrete mock-up and the caller's response; only the caller settles that preference.

Return the observation, its source/configuration, limitations, and the decision or next question it supports. Preserve failed predictions as evidence. If a documented native path needs a deviation, retain its reason and support in the existing intent. Keep only decision-relevant details and pointers in the existing handoff, not the whole experiment transcript. Disposable work is not a production implementation; retain or remove it under the caller's ownership and storage policy. New proof uses the existing protected external storage boundary when applicable.

Reuse after source drift

Before reusing inherited prototype evidence when discovery resumes, check the current referenced interface and relevant environment assumptions against the observation's recorded source/configuration. Check what could change the named result; a purely unrelated source change does not require repetition.

If a changed assumption could affect the result, preserve the earlier observation with its original context and repeat only the smallest discriminating probe against the current subject before declaring that empirical question resolved. Record the new observation and its limitations in the existing intent or handoff; do not replay the broader research.

If relevant assumptions cannot be checked or required execution is unavailable, leave that empirical question explicitly unresolved. Continue only independent ready work that does not rely on the result.

A prototype cannot change intent, authorize implementation or establish acceptance. Even promising results require implementation checks and fresh exact-content judgment. No new tracker, control ledger or automatic context admission follows from the experiment.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub6d

    The skill defines a structured planning workflow for AI agents, focusing on intent discovery, uncertainty routing, and BDD-style behavioral definitions. It includes a local validation script to ensure instruction consistency and cites external engineering methodologies for context. No security risks, obfuscation, or unauthorized data access patterns were detected.

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    Risk: LOW · No issues

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    Score: 93/100 · 2 sections analyzed

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