AKM Eval — Evidence-based self-assessment for agentic knowledge management

Assess how a person + knowledge repository + agent runtime + operating loops work together. AKM Eval applies the AKM Index v1.2 rubric: five pillars, 25 behaviourally anchored criteria, and a weighted score out of 100.

Skill release: 1.2.2 · Rubric: 1.2 — these version numbers describe different artefacts. The v1.2 specification adds descriptive dimensions; its scoring anchors remain those of v1.1.

Website · Full English rubric · English source · Participation guide

What it assesses

The target is the operating system for knowledge work—not the quality of a single note, a person’s intelligence, or the number of AI tools installed.

Each pillar has five criteria rated 0–4: absent, ad hoc, documented, systematised, and self-improving. The evidence-based audit requires artefact-linked judgements; unsupported claims are capped at level one. Level four requires recorded feedback leading to a system change, not simply more automation. Read the complete anchors and their exceptions before scoring.

The M0–M4 labels are interpretive bands, not validated population norms. The weighted total, ordinal-level arithmetic, N/A handling and boundary conventions should be interpreted with the full specification. A high score is not a safety certification, independent external audit, or proof of improved productivity.

Outputs

• A structured akm-report.json: declared scope and version, environment, criterion ratings, evidence notes, domain totals, strengths, improvement priorities and optional history.
• A Markdown summary and, for submitted reports, an HTML report following the current skill workflow.
• With separate public-display permission, an assessment-board card and—if detailed publication is also permitted—a public report.