75 lines
7.4 KiB
Plaintext
75 lines
7.4 KiB
Plaintext
---
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id: skills-maintenance
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title: Skills maintenance
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description: Review, drift, deprecation, rollback and incident handling.
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hide_table_of_contents: true
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---
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import ArchifyDiagram from '@site/src/components/ArchifyDiagram';
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<div className="strategy-content">
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**TO-BE · Target operating model — not installed controls or company results.**
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Optional skills reference for a retained intervention, not a six-month obligation to establish a catalog program. Map department names to actual agreed functions. The [6 Months Roadmap](./six-months-roadmap.mdx) requires funded upkeep and a willing owner before expansion.
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## What happens when a skill drifts or causes harm?
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Monitor both **Workflow effectiveness** (comparable outcomes, safety, cost and human experience) and **Skills effectiveness & health** (fitness/regression evidence, selection, supported use and substantive review) throughout a revision's life. Use the evidence to improve, retain or retire skills; adoption or current review dates alone do not prove better work. Unsafe behavior triggers containment; a correction returns through independent tests and approval, never automatic reinstatement.
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<ArchifyDiagram name="strategy-maintenance" title="Monitor, triage and test corrections; promote safely or contain and roll back" story />
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- **Named ownership:** Data & AI coordinates catalog health with the owning teams.
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- **Early triggers:** incidents and client, model or dependency changes override the calendar.
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- **Recovery is verified:** restore a known-good bundle when present; first-revision recovery disables/removes the candidate, invalidates/reloads sessions and verifies declared current AI-assisted practice in a pinned clean environment. Record no-approved-revision N/A and independent acceptance under the [Quality rule](./skills-quality.mdx); recovery cannot undo disclosure or past effects.
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## Close the loop on the retained workflow
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Start from [Adoption's decision](./skills-adoption.mdx), the approved manifest and its evidence limits. In the **illustrative Delivery workflow**, a missed regression case becomes a sanitized evaluation case. Product Engineering owns the correction; an independent Quality Engineering reviewer reruns [Quality's fitness and regression evaluation](./skills-quality.mdx) against the approved revision. Keep the live defect and task records in their original populations; do not erase a failure because the replacement passes.
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The owning team's designated approver decides whether the corrected exact revision can re-enter a bounded canary, using independent results and Platform & IT's recovery/effective-version evidence; Architecture & Security reviews changed risk boundaries. Missing evidence means hold. Safe unchanged use may continue only within its existing authorization; harmful behavior is contained immediately. These recommended functions require named people before use.
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Retain a supported revision, requalify a change or retire it with a reconciled consumer/migration record. A materially changed workflow returns to [Baseline](./baseline.mdx); the [Measurement registry](./measurement/index.mdx) helps revise relevant evidence without silently resetting history. That feedback closes [Stage 01](./stage-01-skills-adoption.mdx)—it does not automatically advance to autonomy.
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<details>
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<summary>Review responsibilities, incident handling and proposed review intervals</summary>
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**TO-BE · Target operating model.** Maintenance sustains the [Stage 01 journey](./stage-01-skills-adoption.mdx); these pages do not describe installed controls or measured company results.
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Every approved skill has an accountable owning team, independent reviewer, review due date, supported client matrix and rollback bundle. Review both usefulness and safety, not just metadata freshness. Proposed maximum review intervals are recorded by risk tier; incidents, dependency changes, model/client drift or repeated failures trigger earlier review.
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Data & AI reviews catalog health with the owning teams. Quality Engineering samples accepted outputs and adds confirmed failures to regression sets without exposing sensitive content. Platform & IT watches unsupported, deprecated and revoked revisions and tests distribution recovery. Architecture & Security reviews emerging misuse and exception expiry. Remove redundant or unused entries deliberately, after examining whether poor discoverability or fit explains non-use.
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A reported unsafe behavior opens an incident with artifact, policy and client versions. Architecture & Security coordinates containment; Platform & IT revokes authority, stops scheduling and quarantines affected revisions; the owning department handles customer or business consequences; Quality Engineering reproduces the failure; Data & AI coordinates catalog correction and trend analysis. Preserve minimal restricted evidence and involve People & Administration Squad for employee-data concerns and Finance & Accounting Squad for financial effects. Restart requires a reviewed fix, regression evidence, verified rollback/revocation and accountable human authorization. Closing the incident does not erase the historical failure from measurement.
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## Proposed maximum review intervals
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Owner ratification is required before adoption. Review earlier after incidents or material dependency changes.
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| Tier | Maximum days between substantive reviews |
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| --- | --- |
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| R1 | 90 |
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| R2 | 60 |
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| R3 | 30 |
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</details>
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<details>
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<summary>Ongoing reporting and comparison</summary>
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## Keep selected evidence useful
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Continue the measures agreed for the workflow in [Baseline](./baseline.mdx) and applied during [Adoption](./skills-adoption.mdx). Revisit relevance when scope changes; the [registry](./measurement/index.mdx) is a source of choices, not a requirement to collect every metric. Preserve current-workflow comparisons separately from named skill/version evaluations. No applicable skill population is N/A, not zero; missing evidence for existing revisions is unknown and must not improve reported health.
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If substantive-review coverage is selected, join each active approved catalog revision to its owner, substantive review evidence and due date. Apply [review coverage](./measurement/skills-within-substantive-review-window.mdx): revisions with all three current / all active approved revisions × 100. The proposed Data & AI review is monthly and at each release, subject to agreement. Report overdue/ownerless revisions and deprecated or revoked versions still in use—not just a percentage.
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At each agreed report, compare like workflows with their recorded starting position. Publish period, counts, exclusions, source coverage and changes in calculation or scope. Zero denominators mean “not available”; unresolved gaps remain visible. Trends are descriptive, not causal ROI. Reconcile late evidence using versioned corrections and distinguish changed practice from changed measurement.
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Act on critical policy effects immediately; do not wait for a reporting cycle. Use selection precision/recall at qualification, or service-level delivery and departmental outcome measures, only where relevant and their source links are feasible. Confirm purpose, lawful basis, access and retention before changing collection, following the [shared safeguards](./measurement/collection-and-safeguards.mdx).
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</details>
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**Continue the loop:** [Requalify changed skills →](./skills-quality.mdx). For a separate future authority decision, read [Beyond 6 Months](./stage-02-autonomous-agent.mdx).
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</div>
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