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    Data Correction Evidence Review

    1

    It performs the supplied case directly, separates verified evidence from unknowns, produces bounded actions, and ties closure to case specific proof rather than generic advice.

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    Data Correction Evidence Review

    Data Correction Evidence Review

    Example session with this skill installed

    Operations wants to correct 1,200 customer records after a bad import. A spreadsheet contains proposed values, but the source of truth is mixed across two systems, no approver is named, and nobody has checked whether corrected values will retrigger billing exports.

    • Read your context and instructions
    • Compiled the data correction evidence

    DATA CORRECTION EVIDENCE REVIEW

    Case analyzed

    Operations wants to correct 1,200 customer records after a bad import. A spreadsheet contains proposed values, but the source of truth is mixed across two systems, no approver is named, and nobody has checked whether corrected values will retrigger billing exports.

    This review uses only the supplied facts. It does not assume access to production systems, private records, customer systems, vendor portals, or approvals that were not provided.

    Verdict

    NOT READY TO CLOSE. The case contains a material control or evidence gap that must be resolved before the workflow can be treated as complete.

    Verified evidence

    • VERIFIED: The scenario and constraints above were explicitly supplied.
    • VERIFIED: At least one required ownership, sequencing, evidence, or validation condition is missing or inconsistent.
    • VERIFIED: No additional approval or successful execution was supplied.
    • UNKNOWN: Any fact not present in the case remains unknown and must not be invented.

    Concrete findings

    1. The supplied case contains enough evidence to identify a control gap, but not enough to claim the workflow is safe or complete.
    2. The highest risk is the mismatch between the stated operating intent and the actual evidence or state described in the case.
    3. Any remediation must preserve the historical record and separate verified facts from assumptions introduced during repair.
    4. Closure should depend on deterministic evidence tied to this exact case rather than a status field or verbal confirmation.

    The controlling rule for this case is

    A data correction is defensible only when the source error, affected scope, authorization, transformation, downstream impact, validation, and audit record are traceable end to end.

    Required actions

    1. Capture the current state for source error and affected records and identify the accountable owner.
    2. Reconcile authorization and ownership against the intended rule and document every material mismatch.
    3. Create a bounded correction for correction method without rewriting or inventing historical evidence.
    4. Validate downstream impact using a repeatable check tied to the supplied scenario.
    5. Do not close the case until validation and audit trail has explicit evidence and any remaining exception is named.

    Deterministic validation

    • A reviewer can reproduce the conclusion from the supplied evidence without relying on private context.
    • Every material mismatch has an owner and a specific corrective action.
    • Unknown facts remain labeled UNKNOWN rather than being inferred as completed work.
    • The final state can be checked independently against the stated acceptance condition.
    • No production verification, approval, customer acceptance, or execution result is claimed unless it was actually supplied.

    Evidence boundaries

    • UNKNOWN: Details not explicitly present in the supplied case.
    • UNKNOWN: Whether proposed corrections have been executed.
    • UNKNOWN: Whether downstream owners have accepted the repaired state.
    • UNKNOWN: Whether production behavior matches the intended control.

    These unknowns do not erase the conclusion. They define the exact evidence required before closure without guessing.

    Decision and handoff

    Keep the case open until the required evidence is attached, the responsible owner is identifiable, and the acceptance condition is demonstrably satisfied. Preserve the original record and append corrections rather than silently rewriting history.

    If the workflow changes later, retain the superseded reasoning and link the new decision to the evidence that caused the change. A later reviewer should be able to reconstruct the chain from request to evidence, decision, action, and verification.

    Closure rule

    Close only when the requested result is supported by evidence. A completed status, saved form, successful navigation, or verbal assurance is not sufficient on its own. The closure record must show what was checked, what changed, who owns the result, and which condition was actually satisfied.

    Acceptance detail

    The final review should explicitly revisit these dimensions: source error and affected records, authorization and ownership, correction method, downstream impact, validation and audit trail. For each one, record the source evidence, current state, expected state, difference, owner, and validation result. If any dimension remains unknown, keep it open or record a bounded exception with an expiry and owner.

    Final conclusion

    The correct disposition for this supplied case is to continue remediation, not to declare success. The evidence is sufficient to identify the gap and define the next actions, but it is not sufficient to claim the repaired state already exists.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Data Correction Evidence Review is a ToolForge Labs workflow to review data corrections for source evidence, scope, authorization, reproducibility, downstream impact, validation, and auditability. It performs the supplied case directly, separates verified evidence from unknowns, produces bounded actions, and ties closure to case specific proof rather than generic advice.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Data & Databases.

    Cannot execute live DB changes or verify external permissions. Relies entirely on the evidence and logs provided in the session context.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

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    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Permissions

    Terminal / Shell
    Read Files
    Write Files

    Use a controlled workspace or supplied evidence. Review owner decisions and proposed changes before operational action.

    Listed1 month ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill automates the rigorous vetting of database changes by checking for source evidence, scope accuracy, and downstream impacts, preventing unverified or unauthorized data alterations.

    The skill is designed for use within ToolForge Labs workflows and is compatible with any AI agent capable of executing structured diagnostic tasks and SQL-based auditing.

    Unlike generic advisors, this skill produces a concrete case verdict and deterministic validation rules tied to specific evidence rather than broad suggestions.

    You will receive the full workflow configuration, including the logic for evidence verification, corrective action mapping, and the defensible closure rule framework.

    Setup involves importing the workflow into your ToolForge environment and connecting it to your data source; no complex manual coding is required to begin the review process.

    The workflow provides a structured output of verified evidence and corrective actions, which can be exported to your internal ticketing or compliance systems for final human approval.

    New in Data & Databases