Summary
| Metric |
Value |
| Issues analyzed |
165 |
| Closed issues |
98 |
| Open issues |
67 |
| Delivered Impact |
245 |
| Remaining Impact |
116 |
| Completion |
67.9% |
Analysis window: last 180 days (since 2025-12-10)
Top closed issues by value
| Issue |
Value signal |
Value |
Labels |
| #37902: [copilot-opt] Retry-blocked: [WIP] Fix failing GitHub Action... |
high-priority |
50 |
bug, automation, high-priority, optimization, cookie, copilot-opt |
| #37901: [copilot-opt] 56% of workflow runs blocked by action_require... |
high-priority |
50 |
automation, high-priority, optimization, cookie, copilot-opt |
| #37900: [copilot-opt] Duplicate concurrent workflow instances on Age... |
high-priority |
50 |
automation, high-priority, optimization, cookie, copilot-opt |
| #37903: [aw] Smoke Copilot - AOAI (apikey) failed |
unknown |
1 |
agentic-workflows |
| #37876: [aw] Matt Pocock Skills Reviewer failed |
unknown |
1 |
agentic-workflows |
| #37875: [aw] Daily SPDD Spec Planner failed |
unknown |
1 |
agentic-workflows |
| #37872: [aw] Repository Tree Map Generator failed |
unknown |
1 |
agentic-workflows |
| #37871: [aw] Test Quality Sentinel failed |
unknown |
1 |
agentic-workflows |
| #37870: [aw] UK AI Operational Resilience failed |
unknown |
1 |
agentic-workflows |
| #37867: [formal-spec] awf-config-sources-spec.md — Formal model... |
unknown |
1 |
automation, testing, specifications, formal-verification |
Top open issues by value
| Issue |
Value signal |
Value |
Labels |
| #37763: [P1] CJS typecheck re-failing on main — Jun 8 regression |
priority-p1 |
50 |
workflow-health, priority-p1, cookie |
| #37910: [aw] agentic workflows out of sync |
unknown |
1 |
maintenance, agentic-workflows |
| #37909: [aw] Daily Cache Strategy Analyzer failed |
unknown |
1 |
agentic-workflows |
| #37907: [aw] Copilot Agent PR Analysis failed |
unknown |
1 |
agentic-workflows |
| #37899: [aw] No-Op Runs |
unknown |
1 |
agentic-workflows |
| #37894: [testify-expert] Improve Test Quality: pkg/cli/deps_test.go |
unknown |
1 |
testing, code-quality, automated-analysis, cookie |
| #37893: [q] Always key mermaid nodes; never delete mermaid diagrams |
unknown |
1 |
automation, agentic-workflows, workflow-optimization |
| #37884: Bump MCP Gateway (mcpg) to v0.3.25 |
unknown |
1 |
mcp, dependencies |
| #37866: Agent Persona Exploration - 2026-06-08 |
unknown |
1 |
agent-research |
| #37855: [deep-report] Consolidate cascade-attributed [aw] failed issues |
unknown |
1 |
automation, quick-win, improvement, cookie |
Interpretation
With 67.9% completion (245 of 361 impact points delivered), the repository is actively resolving its tracked objectives. However, only 4 of 165 issues (2.4%) carry a priority or severity label, meaning 97.6% of all issues fall back to the unknown = 1 baseline value. As a result, the completion metric is dominated by volume rather than weighted priority — the three closed high-priority issues each contribute 50 points while the remaining 95 closed issues contribute just 1 point each. This compression makes the delivery signal nearly indistinguishable from a simple issue-close rate.
For maintainers and PMs: the model is structurally sound but needs richer labeling to be actionable. Once priority labels are applied consistently, the completion percentage would reflect whether high-impact work is being closed ahead of lower-impact work.
Data quality
Priority and severity labels are rarely used in this repository. Out of 165 issues analyzed:
high-priority — 3 issues (P1 = 50, all closed)
priority-p1 — 1 issue (P1 = 50, open)
- All other priority/severity patterns — 0 issues
The vast majority of labels are classification labels (agentic-workflows, automation, cookie, bug, etc.) which carry no priority weight under this model. The repository needs consistent priority or severity labeling for this metric to be meaningful. Consider adopting a labeling convention such as priority: high/medium/low or P0–P3 and applying it to new and existing issues.
Generated by Objective Impact Report workflow — 2026-06-08
Generated by 📊 Objective Impact Report · sonnet46 1.4M · ◷
Summary
Top closed issues by value
high-prioritybug,automation,high-priority,optimization,cookie,copilot-opthigh-priorityautomation,high-priority,optimization,cookie,copilot-opthigh-priorityautomation,high-priority,optimization,cookie,copilot-optunknownagentic-workflowsunknownagentic-workflowsunknownagentic-workflowsunknownagentic-workflowsunknownagentic-workflowsunknownagentic-workflowsunknownautomation,testing,specifications,formal-verificationTop open issues by value
priority-p1workflow-health,priority-p1,cookieunknownmaintenance,agentic-workflowsunknownagentic-workflowsunknownagentic-workflowsunknownagentic-workflowsunknowntesting,code-quality,automated-analysis,cookieunknownautomation,agentic-workflows,workflow-optimizationunknownmcp,dependenciesunknownagent-researchunknownautomation,quick-win,improvement,cookieInterpretation
With 67.9% completion (245 of 361 impact points delivered), the repository is actively resolving its tracked objectives. However, only 4 of 165 issues (2.4%) carry a priority or severity label, meaning 97.6% of all issues fall back to the
unknown = 1baseline value. As a result, the completion metric is dominated by volume rather than weighted priority — the three closedhigh-priorityissues each contribute 50 points while the remaining 95 closed issues contribute just 1 point each. This compression makes the delivery signal nearly indistinguishable from a simple issue-close rate.For maintainers and PMs: the model is structurally sound but needs richer labeling to be actionable. Once priority labels are applied consistently, the completion percentage would reflect whether high-impact work is being closed ahead of lower-impact work.
Data quality
Priority and severity labels are rarely used in this repository. Out of 165 issues analyzed:
high-priority— 3 issues (P1 = 50, all closed)priority-p1— 1 issue (P1 = 50, open)The vast majority of labels are classification labels (
agentic-workflows,automation,cookie,bug, etc.) which carry no priority weight under this model. The repository needs consistent priority or severity labeling for this metric to be meaningful. Consider adopting a labeling convention such aspriority: high/medium/loworP0–P3and applying it to new and existing issues.Generated by Objective Impact Report workflow — 2026-06-08