Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/samibs/skillfoundry/analyticsnpx skills add samibs/skillfoundry --skill analyticsgit clone --depth 1 https://github.com/samibs/skillfoundryWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00008 | $0.04780 |
| Opus 5 | $0.00004 | $0.02390 |
| Sonnet 5 | $0.00002 | $0.00956 |
| Haiku 4.5 | $0.00001 | $0.00478 |
Grade A, and why
analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 531 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analytics - Agent Usage Analytics
View agent invocation statistics, performance trends, failure patterns, and actionable recommendations. The unvarnished 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 <name> 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:
{
"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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 531 lines · 8 tokens per session scan A 2f402beff20f
analytics is a skill published in the GitHub repository samibs/skillfoundry (12 stars, last pushed 1mo ago), licensed MIT. It adds 8 tokens to every session and 4,780 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
memorix-troubleshooting
Use when Memorix MCP, setup, project binding, HTTP control plane, hooks, skills, or agent integration is missing, stale, or failing.
memorix-sessions
Use when resuming work, preparing handoff context, binding an HTTP control-plane project, or deciding whether sessionstart is useful.
ring:applying-composition-patterns
React composition patterns that scale. Avoid boolean prop proliferation by using compound components, lifting state, and composing internals. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or during architecture review. Skip for simple components with 1-2 props…
ring:searching-code
Forensic code search and analysis with optional Chain of Draft (CoD) ultra-concise mode. Five-phase methodology (clarification, planning, execution, analysis, synthesis) with severity assessment. Use for targeted investigation of specific patterns, bugs, or vulnerabilities. Skip for broad architecture mapping (use…
ring:exploring-codebases
Exploring a codebase across phases: scopes the target, detects architecture, components, and layers, deep-dives each discovered perspective, then synthesizes findings into actionable guidance with file:line evidence. Use to understand how a feature or system works before planning changes, or to orient on an unfamiliar…
ring:auditing-dependency-security
Auditing a dependency for supply-chain risk before install (pip/npm/go/cargo): checks typosquatting, maintainer/age risk, vulnerability DBs (OSV, GHSA, Socket), and lockfile hash pinning, then emits a risk score and approve/conditional/escalate/block decision. Use when adding or updating a dependency, reviewing a…