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 skills add samibs/skillfoundry --skill analyticsgit clone --depth 1 https://github.com/samibs/skillfoundryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/samibs/skillfoundry/analytics)<a href="https://agentmods.dev/skills/samibs/skillfoundry/analytics"><img src="https://agentmods.dev/badge/skills/samibs/skillfoundry/analytics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/samibs/skillfoundry/analytics"><img src="https://agentmods.dev/badge/skills/samibs/skillfoundry/analytics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00008 | $0.04780 |
| Opus 5.5 | $0.00003 | $0.01912 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 14d ago), licensed MIT. It adds 8 tokens to every session and 4,780 once invoked, about $0.0000 per session on Opus 5.5. 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.
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