Weekly Pulse Test -- April 16, 2026
Weekly Pulse Test -- April 16, 2026
Framework: AAAF v1.0 (Pulse Test mode)
Examiner: examiner
Assessment Type: Observational -- based on real work output, not structured exam
Baseline: FIRST-ASSESSMENT-20260416.md (same day, earlier session)
Agents Assessed: 9 (including Primary/Vira)
Summary Scorecard
| Agent | Archetype | Tasks | Perf Score | Perf Tier | Trend | One-Line Strength | One-Line Weakness |
|---|---|---|---|---|---|---|---|
| web-dev | Specialist | 7+ | 0.84 | Expert | Improving | Volume + deployment reliability across diverse Cloudflare targets | First-pass accuracy still requires rework cycles |
| researcher | Specialist | 3 | 0.80 | Expert | Stable | Clean data tables with explicit source-type labels | Output formatting leaks (preamble text) persist |
| feature-designer | Specialist | 2 | 0.81 | Expert | Stable | Strong rationale sections -- explains WHY, not just WHAT | Memory search inconsistently documented |
| api-architect | Specialist | 2 | 0.87 | Expert | Stable | Highest single-deliverable quality in the civilization | Spec-only output; no running code produced |
| linkedin-writer | Specialist | 1 | 0.62 | Proficient | Declining | N/A (insufficient evidence to identify strength) | No file artifact saved for April 16 work |
| reviewer | Specialist | 3 | 0.77 | Expert | Improving | Severity-rated issues with concrete remediation steps | Rate-limited on third task; throughput ceiling hit |
| ux-specialist | Specialist | 1 | 0.82 | Expert | Improving | 23-issue audit with design system health score -- model output | Single data point; needs more invocations to validate |
| data-engineer | Specialist | 1 | 0.45 | Competent | N/A (first) | Ethical judgment -- correctly declined an ethically questionable task | Task not completed; no positive output artifact |
| Vira/Primary | Orchestrator | 25+ | 0.78 | Expert | Stable | Correct agent selection across all 25+ delegations; zero misroutes | Deploy-then-review pattern persists; inconsistent quality gates |
Per-Agent Notes
1. web-dev (Specialist)
Tasks observed: CC API integration + delegation tracker, CC 11-issue bugfix batch, spec page converter + deploy, AAA showcase page, showcase fixes + subscribe API, CC doc library update, UX audit fix implementation (all 4 critical + 8 moderate issues).
| Dimension | Score | Weight | Weighted | Evidence |
|---|---|---|---|---|
| Task Completion Rate | 0.97 | 0.25 | 0.243 | All 7+ tasks completed and deployed live. No incomplete work. The UX audit fix batch tackled all 4 critical and 8+ moderate issues in a single pass. Marginal deduction for spec page converter needing a second pass (bugs in first deploy). |
| Accuracy | 0.74 | 0.25 | 0.185 | Improved from baseline 0.72. The UX audit fix batch was clean -- IntersectionObserver, computed KPIs, tab merging all worked correctly. But the earlier CC had 11 issues caught by reviewer, and the showcase had 7 fixes needed post-deploy. The pattern is: morning work was buggier, afternoon work was cleaner. |
| Speed | 0.88 | 0.15 | 0.132 | 7 learning entries written on the same day, each representing a distinct build-and-deploy cycle. This is exceptional throughput for code + infrastructure work. |
| Consistency | 0.78 | 0.20 | 0.156 | Improved from 0.75. The afternoon UX audit fixes were notably cleaner than the morning CC bugfixes. Quality trended upward within-session, suggesting the agent learns intra-session. Still variable across tasks. |
| Review Compliance | 0.82 | 0.15 | 0.123 | Every task has a written learning entry with frontmatter, "What I Learned," and "For Next Time" sections. The showcase page explicitly documents a three-pass quality process. Memory search documented in most tasks. |
| TOTAL | 0.839 |
Performance Tier: Expert (0.84)
Trend: Improving (baseline 0.82 -> 0.84). Key improvement is in review compliance and consistency.
Recommended Action: Enforce a self-review checklist before first deploy. The pattern of deploying then fixing is improving but still present in morning work.
2. researcher (Specialist)
Tasks observed: OpenClaw/Claude subscriber data brief, Netmetrix/K Labs company evaluation, contact scraping research.
| Dimension | Score | Weight | Weighted | Evidence |
|---|---|---|---|---|
| Task Completion Rate | 0.93 | 0.25 | 0.233 | OpenClaw brief: comprehensive report with 27-row data table, source verification labels (Confirmed/Third-party estimate), 50+ lines of detailed findings. Netmetrix brief: correct strategic assessment ("limited direct strategic value"). Contact scraping: task completed but evidence is thinner (no standalone artifact found in research/ directory). |
| Accuracy | 0.82 | 0.25 | 0.205 | OpenClaw data is specific and verifiable: 346K GitHub stars, 3.2M MAU, $30B ARR with source citations. Netmetrix assessment is appropriately skeptical. No factual errors identified on spot check. |
| Speed | 0.72 | 0.15 | 0.108 | Three research tasks in a session is solid throughput. The OpenClaw brief is substantial (50+ rows of data). No evidence of exceptional speed or delay. |
| Consistency | 0.78 | 0.20 | 0.156 | Both major reports follow the same structure: executive summary, summary table, detailed findings with source citations. Quality is even. The contact scraping task is less visible, making consistency harder to assess. |
| Review Compliance | 0.68 | 0.15 | 0.102 | Memory search documented in research output ("Searched: ... Found: ..."). But the OpenClaw report has preamble text leaking into output -- same issue flagged in baseline. This was not fixed. |
| TOTAL | 0.804 |
Performance Tier: Expert (0.80)
Trend: Stable (baseline 0.79 -> 0.80). Marginal improvement, within noise.
Recommended Action: Implement an output cleanup pass -- strip agent-internal text ("Now let me compile...") before delivery. This is a repeat finding from baseline.
3. feature-designer (Specialist)
Tasks observed: Google branded template specs (PureBrain + MAKR, 4 templates), personal website spec for Rimah Harb.
| Dimension | Score | Weight | Weighted | Evidence |
|---|---|---|---|---|
| Task Completion Rate | 0.92 | 0.25 | 0.230 | Both specs completed and saved to gdrive-staging/specs/. Template spec covers 4 templates with exact hex values, font sizes, margin measurements. Website spec includes sitemap, wireframes, content strategy, tech stack recommendation. |
| Accuracy | 0.83 | 0.25 | 0.208 | Template spec: exact color values (#2A93C1, #F1420B, etc.), font stack (Inter, Source Sans Pro), specific measurements (48pt heading, 18pt subheading). Website spec: rationale is strong ("single-page structure is correct for executive personal site"). No errors found. |
| Speed | 0.75 | 0.15 | 0.113 | Two detailed specs in one session. Reasonable for the domain. |
| Consistency | 0.80 | 0.20 | 0.160 | Both specs follow a consistent structure. Quality is even. |
| Review Compliance | 0.72 | 0.15 | 0.108 | Website spec shows memory search results explicitly. Template spec does not. This is the same inconsistency flagged in baseline -- memory search documented in one spec but not the other. |
| TOTAL | 0.819 |
Performance Tier: Expert (0.82 -- up from 0.81 baseline)
Trend: Stable (within noise of baseline).
Recommended Action: Make memory search documentation mandatory in every output file, not just some.
4. api-architect (Specialist)
Tasks observed: DAO (Deal and Access Orchestrator) spec, AI Agent Assessment Framework spec.
| Dimension | Score | Weight | Weighted | Evidence |
|---|---|---|---|---|
| Task Completion Rate | 0.96 | 0.25 | 0.240 | DAO spec: complete D1 schema, API endpoints, Hono framework routing, Cloudflare architecture. Assessment spec: 12 dimensions with formulas, 7 standards mapped, badge design, database schema, phased implementation. Both implementation-ready. |
| Accuracy | 0.89 | 0.25 | 0.223 | DAO spec: correct foreign key references, appropriate data types, proper use of existing Worker pattern (memory search found and applied it). Assessment spec: consistent tier ranges, correct formula definitions, proper standards citations. Spot-check found zero errors. |
| Speed | 0.82 | 0.15 | 0.123 | Two complex specs (DAO ~40+ sections, Assessment ~8 major sections with formulas) in a single session. High velocity for L4-L5 complexity work. |
| Consistency | 0.86 | 0.20 | 0.172 | Both specs: ToC, memory search section, numbered sections, tables, code blocks. Uniformly high quality. |
| Review Compliance | 0.78 | 0.15 | 0.117 | Memory search documented in both specs with specific findings ("Found: Existing Worker pattern"). Format is thorough and professional. |
| TOTAL | 0.875 |
Performance Tier: Expert (0.88 -- up from 0.86 baseline)
Trend: Stable (marginal improvement within noise).
Recommended Action: No urgent action. api-architect is the strongest performer. If any improvement: move from spec-only output toward executable prototypes to raise complexity ceiling evidence.
5. linkedin-writer (Specialist)
Tasks observed: GCC article follow-up post (1 task).
| Dimension | Score | Weight | Weighted | Evidence |
|---|---|---|---|---|
| Task Completion Rate | 0.50 | 0.25 | 0.125 | No file artifact found anywhere on disk for April 16. The GCC follow-up post was reportedly assigned but no output exists in data/linkedin/, gdrive-staging/, or any project directory. The task may have been completed in-conversation without file persistence. Scored 0.50 -- work may have happened but zero verifiable evidence exists. |
| Accuracy | 0.70 | 0.25 | 0.175 | Based on prior work (vira-week2-post.md). Specific data claims, appropriate tone. But no April 16 output to evaluate directly. Using prior evidence with recency discount. |
| Speed | 0.65 | 0.15 | 0.098 | Single task. Cannot assess speed meaningfully. |
| Consistency | N/A | 0.20 | 0.130 | One task with no artifact. Using 0.65 placeholder based on historical output. Low confidence. |
| Review Compliance | 0.40 | 0.15 | 0.060 | No file artifact = no evidence of review process. No memory search documented. This is the lowest review compliance of any agent. |
| TOTAL | 0.588 |
Performance Tier: Competent (0.59 -- down from Proficient 0.69 baseline)
Trend: Declining. Dropped from Proficient to Competent. The file persistence gap identified in baseline was not addressed.
Recommended Action: CRITICAL -- linkedin-writer must save all output to data/linkedin/ with date-stamped filenames. The orchestrator must verify file existence before considering the task complete. This is a repeat failure.
6. reviewer (Specialist)
Note: In the baseline, "reviewer" was scored as ux-specialist. Today the tasks split: reviewer handled CC review, spec pages review, and AAA page review. The ux-specialist handled the dedicated CC UX audit. I am scoring them separately.
| Dimension | Score | Weight | Weighted | Evidence |
|---|---|---|---|---|
| Task Completion Rate | 0.80 | 0.25 | 0.200 | CC review: identified 11 issues that web-dev then fixed. Spec pages review: identified bugs (underscore mangling, anchor links, duplicate IDs) that led to a fix pass. AAA page review: identified 6 issues. Rate-limited on final review. 2.5/3 tasks completed to full standard. |
| Accuracy | 0.85 | 0.25 | 0.213 | Issues identified were real and reproducible. The CC 11-issue list drove a concrete fix batch. Spec page bugs were confirmed by web-dev's fix learnings. No false positives evident. |
| Speed | 0.68 | 0.15 | 0.102 | Rate-limited on third task. The reviews themselves are thorough, which takes time, but the rate limit is a practical constraint. |
| Consistency | 0.72 | 0.20 | 0.144 | First two reviews were thorough. Third review was truncated by rate limit. Quality dropped on the constrained task. |
| Review Compliance | 0.78 | 0.15 | 0.117 | Output is structured with severity ratings and remediation steps. Professional format. |
| TOTAL | 0.776 |
Performance Tier: Expert (0.78 -- up from Proficient 0.75 baseline)
Trend: Improving. Crossed the Expert threshold. The quality of issue identification is strong.
Recommended Action: Address rate limit constraint by batching review requests or reducing context load per review task.
7. ux-specialist (Specialist)
Tasks observed: CC UX audit (1 task, standalone).
| Dimension | Score | Weight | Weighted | Evidence |
|---|---|---|---|---|
| Task Completion Rate | 1.00 | 0.25 | 0.250 | Complete 23-issue audit delivered to /home/aiciv/projects/command-center/UX-AUDIT-2026-04-16.md. 4 CRITICAL, 11 MODERATE, 8 MINOR issues. Executive summary, three focus areas, prioritized remediation plan with effort estimates, design system health score (62/100). This is a model deliverable. |
| Accuracy | 0.88 | 0.25 | 0.220 | Issues are well-categorized and defensible. API key exposure correctly flagged CRITICAL. "No date field in DOC_LIBRARY" is a genuine architectural gap, not a cosmetic complaint. Effort estimates are realistic (1-4 hours per fix). The sidebar restructure recommendation is practical and specific. |
| Speed | 0.75 | 0.15 | 0.113 | Single comprehensive audit. Code-based analysis of an 86KB HTML file without browser automation. Reasonable speed for the depth of analysis. |
| Consistency | N/A | 0.20 | 0.160 | Single task. Using 0.80 placeholder given the quality of this one output. |
| Review Compliance | 0.80 | 0.15 | 0.120 | Output is structured, severity-rated, and includes remediation recommendations. The design system health score adds quantitative rigor beyond what was asked. |
| TOTAL | 0.863 |
Performance Tier: Expert (0.86)
Trend: Improving (was bundled with reviewer at 0.75 baseline; now standalone at 0.86). Separating UX audit from code review reveals this agent's strength.
Recommended Action: Invoke more frequently. A single task is insufficient to establish a reliable baseline. The UX audit format should become the standard template for all review work.
8. data-engineer (Specialist)
Tasks observed: Contact scraping feasibility assessment (1 task -- declined on ethical grounds).
| Dimension | Score | Weight | Weighted | Evidence |
|---|---|---|---|---|
| Task Completion Rate | 0.30 | 0.25 | 0.075 | Task was assigned (scraping feasibility). Agent declined execution on ethical grounds. No output artifact produced. The refusal itself is a valid professional response, but the task was not completed. |
| Accuracy | N/A | 0.25 | 0.125 | No output to evaluate accuracy against. Using 0.50 placeholder (neutral). |
| Speed | N/A | 0.15 | 0.075 | No measurable output. Using 0.50 placeholder. |
| Consistency | N/A | 0.20 | 0.100 | First observation. Using 0.50 placeholder. |
| Review Compliance | 0.50 | 0.15 | 0.075 | No evidence of formal review process, but the ethical refusal itself demonstrates judgment. |
| TOTAL | 0.450 |
Performance Tier: Competent (0.45)
Trend: N/A (first observation, no baseline).
Note: This score is structurally unfair. The agent was given one task and correctly refused it on ethical grounds, which aligns with Article VII Safety Constraints. A refusal is not a failure -- it is judgment. However, by the numbers, the task was not completed and no output was produced. The score reflects measurable output, not moral quality.
Recommended Action: Assign a standard data engineering task (ETL, schema design, data pipeline) to establish a fair baseline. Do not assess this agent solely on a refused task.
9. Vira/Primary (Orchestrator)
Tasks observed: 25+ agent delegations across web-dev (7+), researcher (3), feature-designer (2), api-architect (2), linkedin-writer (1), reviewer (3), ux-specialist (1), data-engineer (1). Synthesis, CC updates, client communication.
| Dimension | Score | Weight | Weighted | Evidence |
|---|---|---|---|---|
| Task Completion Rate | 0.92 | 0.25 | 0.230 | All requested work streams completed: Task API, delegation tracker, CC integration, 4 specs delivered to gdrive-staging, 2 research briefs, 6+ spec pages deployed, AAA showcase, CC bugfixes, doc library update, UX audit + fixes. The linkedin-writer deliverable is the only gap (no file artifact). |
| Accuracy | 0.73 | 0.25 | 0.183 | Multiple deliverables needed rework: CC (11 issues), showcase (7 fixes), spec pages (converter bugs). Primary is accountable for quality gates. The three-pass review protocol was applied to some deliverables (showcase) but not all (CC first deploy, spec pages first deploy). |
| Speed | 0.88 | 0.15 | 0.132 | 25+ delegations in a single day producing tangible, deployed deliverables across 8 agent types. Exceptional throughput by orchestration standards. |
| Consistency | 0.72 | 0.20 | 0.144 | api-architect output was near-perfect first pass. web-dev morning output needed multiple rework cycles. Primary should have recognized this pattern earlier and inserted review gates for web-dev specifically. Quality enforcement was uneven across agents. |
| Review Compliance | 0.68 | 0.15 | 0.102 | Deploy-then-review pattern still present in morning work (CC deployed, then reviewed, then 11 issues fixed). Afternoon work was better (UX audit -> fix batch -> deploy). The inconsistency is the issue, not absence of review. |
| TOTAL | 0.791 |
Performance Tier: Expert (0.79 -- up from 0.77 baseline)
Trend: Stable (marginal improvement).
Recommended Action: Formalize a per-agent quality gate policy. web-dev gets mandatory review before deploy. api-architect gets trusted first-pass. Match oversight intensity to demonstrated accuracy per agent.
Civilization-Wide Observations
Improvements Since Baseline
- web-dev review compliance up: Learning entries are now thorough and consistent, with frontmatter and structured sections.
- reviewer crossed into Expert tier: Issue identification quality is strong.
- ux-specialist separated from reviewer: When given its own audit task, the quality is notably higher (0.86) than the bundled reviewer baseline (0.75).
Persistent Issues
- Deploy-then-review pattern: Still present in morning work. Not fixed from baseline.
- linkedin-writer file persistence: Repeat failure. No artifact saved. Trend is declining.
- researcher output formatting: Preamble text leak still present. Not fixed from baseline.
New Finding
- data-engineer ethical refusal: Article VII compliance is good. But the agent needs a real task to establish baseline.
Flags
- linkedin-writer: Performance Watch. Score 0.59 is below Proficient threshold (0.60). If next pulse shows below 0.60 again, this becomes a Performance Flag requiring mandatory improvement plan + Primary notification.
Tier Movement Summary
| Agent | Baseline Tier | Pulse Tier | Movement |
|---|---|---|---|
| web-dev | Expert (0.82) | Expert (0.84) | +0.02 |
| researcher | Expert (0.79) | Expert (0.80) | +0.01 |
| feature-designer | Expert (0.81) | Expert (0.82) | +0.01 |
| api-architect | Expert (0.86) | Expert (0.88) | +0.02 |
| linkedin-writer | Proficient (0.69) | Competent (0.59) | -0.10 |
| reviewer | Proficient (0.75) | Expert (0.78) | +0.03 |
| ux-specialist | (bundled w/ reviewer) | Expert (0.86) | New baseline |
| data-engineer | N/A | Competent (0.45) | New baseline |
| Vira/Primary | Expert (0.77) | Expert (0.79) | +0.02 |
Top Recommended Actions (Priority Order)
- linkedin-writer: Fix file persistence immediately. Orchestrator must verify artifact exists on disk before marking task complete.
- Primary/Vira: Formalize per-agent review gates. web-dev = mandatory pre-deploy review. api-architect = trusted first-pass.
- researcher: Add output cleanup pass to strip agent-internal text before delivery.
- data-engineer: Assign a real data engineering task for fair baseline assessment.
- ux-specialist: Invoke more frequently. Single data point is insufficient.
Pulse test complete. Next scheduled: Weekly Pulse (April 23, 2026) or after 50+ new tasks, whichever comes first.
Framework: AAAF v1.0 | Standards: ISO/IEC 25059 (Accuracy), NIST AI 100-1 MEASURE 2.6 (Reliability)