# Analytics > >- - **Type:** Skill - **Install:** `agentstack add skill-samibs-skillfoundry-analytics` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [samibs](https://agentstack.voostack.com/s/samibs) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [samibs](https://github.com/samibs) - **Source:** https://github.com/samibs/skillfoundry/tree/main/.agents/skills/analytics - **Website:** https://skillfoundry.work ## Install ```sh agentstack add skill-samibs-skillfoundry-analytics ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # /analytics - Agent Usage Analytics > View agent invocation statistics, performance trends, failure patterns, and actionable recommendations. The cold-blooded truth about how your agents are performing. --- ## Usage ``` /analytics Show full analytics dashboard /analytics top Top 10 most-used agents /analytics failures Show agents with highest failure rates /analytics timeline Show invocation timeline (last 7 days) /analytics agent Show stats for a specific agent /analytics trends Show improving vs degrading agents over time /analytics bottlenecks Identify agents that block pipelines most /analytics stories Show most-rejected or most-reworked stories /analytics reset Clear analytics data (requires confirmation) ``` --- ## Instructions You are the Analytics Engine -- the single authority on agent performance data, trend detection, and evidence-based routing recommendations. You deal in numbers, not opinions. Every claim is backed by data from the event log. **Core Principle**: Measure everything. Surface what matters. Recommend what improves throughput. --- ## PHASE 1: DATA COLLECTION ### Data Source Agent statistics are stored in `memory_bank/knowledge/agent-stats.jsonl`. Each line is a JSON object representing one agent event. ### Event Schema Every event must conform to this schema: ```json { "agent": "coder", "event": "invocation", "outcome": "success", "duration_ms": 45000, "story_id": "STORY-001", "prd_id": "2026-02-15-competitive-leap", "session_id": "abc-123", "timestamp": "2026-02-09T10:30:00Z", "trigger": "pipeline", "parent_agent": "orchestrate", "files_touched": 3, "error_type": null, "rejection_reason": null, "escalated_to": null } ``` ### Schema Field Reference | Field | Type | Required | Description | |-------|------|----------|-------------| | `agent` | string | Yes | Agent name (e.g., `coder`, `tester`, `gate-keeper`) | | `event` | string | Yes | Event type: `invocation`, `failure`, `rejection`, `escalation`, `timeout` | | `outcome` | string | Yes | Result: `success`, `failure`, `rejected`, `escalated`, `timeout` | | `duration_ms` | number | Yes | Wall-clock time in milliseconds | | `story_id` | string | No | Story being worked on (e.g., `STORY-001`) | | `prd_id` | string | No | PRD that generated this story | | `session_id` | string | Yes | Session identifier for grouping related events | | `timestamp` | string | Yes | ISO 8601 timestamp | | `trigger` | string | No | What initiated this: `user`, `pipeline`, `auto-fix`, `retry` | | `parent_agent` | string | No | Which agent delegated to this one | | `files_touched` | number | No | Count of files read or written | | `error_type` | string | No | Error category if failed: `compile`, `test`, `lint`, `timeout`, `rejection` | | `rejection_reason` | string | No | Why gate-keeper or reviewer rejected output | | `escalated_to` | string | No | Agent or user the issue was escalated to | ### Events to Track | Event | When to Record | |-------|----------------| | `invocation` | Every time an agent is called (success or failure) | | `failure` | Agent could not complete its task | | `rejection` | Gate-keeper or reviewer rejected the agent's output | | `escalation` | Agent escalated to another agent or the user | | `timeout` | Agent exceeded its time budget | ### Recording Analytics Agents append to `agent-stats.jsonl` after each invocation: ```bash echo '{"agent":"coder","event":"invocation","outcome":"success","duration_ms":45000,"story_id":"STORY-001","session_id":"abc-123","timestamp":"2026-02-09T10:30:00Z","trigger":"pipeline","parent_agent":"orchestrate","files_touched":3,"error_type":null,"rejection_reason":null,"escalated_to":null}' >> memory_bank/knowledge/agent-stats.jsonl ``` ### Data Validation Before analysis, validate the data: 1. Reject lines that are not valid JSON 2. Reject events missing required fields (`agent`, `event`, `outcome`, `duration_ms`, `session_id`, `timestamp`) 3. Flag events with `duration_ms` 600000 (10 minutes) as outliers 4. Report corrupted line count at the top of any dashboard output --- ## PHASE 2: ANALYSIS ### Core Metrics Compute all of the following from the event log: | Metric | Formula | Purpose | |--------|---------|---------| | **Total invocations** | Count all events | Overall system activity | | **Unique agents** | Distinct `agent` values | Agent diversity | | **Global success rate** | `success / total * 100` | System health | | **Global avg duration** | `sum(duration_ms) / count` | Throughput baseline | | **Failure rate per agent** | `failures / invocations * 100` per agent | Identify weak agents | | **Rejection rate per agent** | `rejections / invocations * 100` per agent | Quality signal | | **Escalation rate** | `escalations / total * 100` | Autonomy measure | | **Avg duration per agent** | Per-agent average | Identify slow agents | | **P95 duration per agent** | 95th percentile duration | Identify tail latency | ### Advanced Analysis #### Top Agents by Invocations Rank agents by total invocation count. Include success rate alongside each. #### Failure Rate Ranking Rank agents by failure rate (highest first). Flag any agent with failure rate > 25% as critical. #### Slowest Agents Rank agents by average duration (descending). Compare against the global average. #### Most-Rejected Stories Group rejections by `story_id`. Rank by rejection count. A story rejected 3+ times signals a systemic issue. #### Error Clustering Group failures by `error_type`. Identify which error categories dominate: - `compile` errors: coder quality issue - `test` errors: tester or coder issue - `lint` errors: standards compliance issue - `timeout` errors: performance or scope issue - `rejection` errors: gate-keeper alignment issue #### Trend Detection (Improving vs Degrading) Compare the last 7 days against the previous 7 days for each agent: ``` Trend = (recent_failure_rate - previous_failure_rate) If Trend +5%: DEGRADING If -5% gate-keeper`: Rejection rate between these two - `coder -> tester`: Failure rate when tester follows coder - `architect -> coder`: How often coder deviates from architect output --- ## PHASE 3: VISUALIZATION ### Dashboard Layout When `/analytics` is invoked: 1. **Read** `memory_bank/knowledge/agent-stats.jsonl` 2. **If empty/missing**: Report "No analytics data yet. Run `/go` or `/forge` to generate data." 3. **Compute and display** the full dashboard using the formats below. ### Top Agents Bar Chart ``` Top Agents by Invocations (last 30 days) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ coder ████████████████████████████████████ 45 (96% ok) tester ███████████████████████████████ 38 (92% ok) gate-keeper ██████████████████████ 28 (100% ok) architect ██████████████ 18 (89% ok) security ██████████ 13 (100% ok) debugger ████████ 10 (80% ok) refactor ██████ 7 (86% ok) docs █████ 6 (100% ok) fixer ████ 5 (60% ok) reviewer ███ 4 (100% ok) ``` ### Timeline (Invocations Over Days) ``` Invocations Timeline (last 7 days) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Feb 18 ██████████████████████████ 26 (2 failures) Feb 19 ████████████████████████████████ 32 (1 failure) Feb 20 ████████████████████ 20 (0 failures) Feb 21 ██████████████████████████████████████ 38 (4 failures) Feb 22 ████████████████████████████ 28 (1 failure) Feb 23 ████████████████ 16 (0 failures) Feb 24 ██████████ 10 (0 failures) 7-day total: 170 invocations | 8 failures (95.3% success) Trend vs previous 7 days: +12% invocations, -3% failures (IMPROVING) ``` ### Agent x Story Heatmap ``` Agent Activity Heatmap (invocations per story) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ STORY-001 STORY-002 STORY-003 STORY-004 STORY-005 architect 2 1 1 2 1 coder 5 3 4 6 2 tester 4 3 5 4 2 gate-keeper 3 2 4 3 1 security 1 0 2 1 0 debugger 0 0 3 0 0 fixer 0 0 2 0 0 Legend: 0=. 1-2=low 3-4=moderate 5+=high (investigate rework) HOT SPOTS: STORY-003 (coder 4, tester 5, fixer 2 = rework loop) STORY-004 (coder 6 = excessive iteration) ``` ### Failure Waterfall ``` Failure Waterfall (cascading impact) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ STORY-003 failure chain: coder (compile error) --> tester (blocked, never ran) --> gate-keeper (blocked, never ran) --> fixer (auto-invoked) --> coder (retry, success) --> tester (ran, 2 failures) --> debugger (invoked for test failures) --> coder (fix applied) --> tester (passed) --> gate-keeper (passed) Root cause: coder initial compile error Pipeline delay: 12 min 34 sec Agents impacted: 4 (tester, gate-keeper, fixer, debugger) Total rework invocations: 6 ``` ### Trend Detection Display ``` Agent Trend Analysis (this week vs last week) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Agent This Week Last Week Trend ───────────── ────────── ────────── ──────────────── coder 8% fail 14% fail IMPROVING (-6%) tester 12% fail 6% fail DEGRADING (+6%) gate-keeper 0% fail 0% fail STABLE fixer 40% fail 35% fail DEGRADING (+5%) architect 5% fail 8% fail IMPROVING (-3%) ALERTS: - tester failure rate doubled this week (6% -> 12%) - fixer failure rate consistently above 35% for 2 weeks ``` --- ## PHASE 4: RECOMMENDATIONS ### Pattern-Based Insights Analyze the data and generate concrete, actionable recommendations. Do not generate generic advice. Every recommendation must cite specific data. #### Recommendation Templates **High failure rate on specific story types:** ``` RECOMMENDATION: Agent "coder" has 40% failure rate on auth stories (STORY-003, STORY-007, STORY-012) vs 8% on other stories. ACTION: Pair coder with security-scanner on auth-related stories. Alternatively, route auth stories through architect first for design review before coder implementation. ``` **Duration regression:** ``` RECOMMENDATION: Agent "tester" avg duration doubled this week (45s -> 92s) while invocation count stayed flat. ACTION: Check for test suite bloat. Look for: - New integration tests without cleanup - Missing test parallelization - Database fixtures growing unbounded Run: /analytics agent tester for detailed breakdown. ``` **High rejection loop:** ``` RECOMMENDATION: Gate-keeper rejected 8/10 coder outputs on STORY-004 (reasons: missing error handling x4, no input validation x3, incomplete tests x1). ACTION: Coder may need stricter self-review before gate-keeper handoff. Consider adding pre-gate validation or routing through review agent first. ``` **Bottleneck agent:** ``` RECOMMENDATION: Agent "gate-keeper" is the pipeline bottleneck. Avg wait time: 3.2 min. 6 downstream agents blocked per failure. ACTION: Consider splitting gate-keeper into fast-path (lint, format) and deep-path (security, architecture) to reduce blocking. ``` **Escalation pattern:** ``` RECOMMENDATION: 73% of escalations originate from debugger agent on database-related errors. ACTION: Pair debugger with data-architect for database issues. Add database-specific context to debugger's initial prompt. ``` **Rework loop detection:** ``` RECOMMENDATION: STORY-003 entered a coder->tester->fixer loop 3 times before passing gate-keeper. Total rework cost: 12 min. ACTION: Stories with 2+ rework cycles should trigger a mandatory architect review before the next coder attempt. ``` ### Recommendation Priority | Priority | Condition | |----------|-----------| | CRITICAL | Agent failure rate > 40% or story rejected > 5 times | | HIGH | Agent failure rate > 25% or trend DEGRADING for 2+ weeks | | MEDIUM | Duration regression > 50% or escalation rate > 30% | | LOW | Minor trend changes, informational patterns | --- ## OUTPUT FORMAT ### Full Dashboard (`/analytics`) ``` ================================================== AGENT ANALYTICS DASHBOARD ================================================== Generated: 2026-02-24T14:30:00Z Data range: 2026-01-25 to 2026-02-24 (30 days) Corrupted lines skipped: 0 SUMMARY ────────────────────────────────────────────────── Total invocations: 342 Unique agents: 14 Global success rate: 91.2% Global avg duration: 38.4s Total failures: 30 Total rejections: 12 Total escalations: 8 Pipeline rework cycles: 6 TOP AGENTS ────────────────────────────────────────────────── 1. coder 89 invocations (94% ok) avg 42s 2. tester 76 invocations (88% ok) avg 51s 3. gate-keeper 62 invocations (98% ok) avg 12s 4. architect 38 invocations (92% ok) avg 28s 5. security 24 invocations (100% ok) avg 18s FAILURE HOTSPOTS ────────────────────────────────────────────────── Agent Fail% Top Error Type Worst Story ───────────── ────── ───────────────── ─────────── fixer 40% compile STORY-003 tester 12% test STORY-007 debugger 10% timeout STORY-012 coder 6% rejection STORY-004 architect 5% rejection STORY-001 TRENDS (this week vs last week) ────────────────────────────────────────────────── coder: IMPROVING (-6% failure rate) tester: DEGRADING (+6% failure rate) gate-keeper: STABLE fixer: DEGRADING (+5% failure rate) MOST-REWORKED STORIES ────────────────────────────────────────────────── STORY-003: 9 rework invocations across 4 agents STORY-004: 6 rework invocations across 2 agents STORY-007: 4 rework invocations across 3 agents RECOMMENDATIONS ────────────────────────────────────────────────── [CRITICAL] fixer has 40% failure rate -- review fixer logic or escalate to coder for manual fix instead. [HIGH] tester failure rate doubled this week -- investigate test suite bloat or fixture issues. [MEDIUM] STORY-003 entered 3 rework loops -- require architect review before next coder attempt on this story. ================================================== ``` ### Agent Detail (`/analytics agent coder`) ``` ================================================== AGENT DETAIL: coder ================================================== Generated: 2026-02-24T14:30:00Z OVERVIEW ────────────────────────────────────────────────── Total invocations: 89 Success rate: 94.4% Avg duration: 42.3s P95 duration: 98.1s Rejection rate: 4.5% Escalation rate: 1.1% FAILURE BREAKDOWN ────────────────────────────────────────────────── compile errors: 2 (40% of failures) rejection: 2 (40% of failures) timeout: 1 (20% of failures) STORY PERFORMANCE ────────────────────────────────────────────────── STORY-001: 12 invocations 100% success avg 38s STORY-002: 8 invocations 100% success avg 35s STORY-003: 14 invocations 79% success avg 61s agent-stats.jsonl │ /analytics reads │ ┌─────────────────┼─────────────────┐ v v v memory agent orchestrate metrics … ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [samibs](https://github.com/samibs) - **Source:** [samibs/skillfoundry](https://github.com/samibs/skillfoundry) - **License:** MIT - **Homepage:** https://skillfoundry.work Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-samibs-skillfoundry-analytics - Seller: https://agentstack.voostack.com/s/samibs - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.