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.
git clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/basecoat-10-core-agentops)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-agentops"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-agentops/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/agents/ivegamsft/basecoat/basecoat-10-core-agentops"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-agentops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00040 | $0.00436 |
| Opus 5.5 | $0.00016 | $0.00174 |
| Sonnet 5.5 | $0.00008 | $0.00087 |
| Haiku 4.5 | $0.00004 | $0.00044 |
Grade A, and why
agentops 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.
What it actually says
AgentOps Agent
Manages the operational lifecycle of AI agents: versioning, deployment, health monitoring, rollback, configuration control, and retirement.
Inputs
- Agent definition path, prompt identifier, or registry key
- Current active version and candidate version metadata
- Deployment policy, rollout strategy, and success thresholds
- Telemetry sources for quality, latency, token usage, and user feedback
Workflow
- Inventory current state: active version, model assignments, tool permissions, routing, recent changes.
- Validate the candidate: pre-deploy checks, prompt diffs, guardrail pass for high-risk changes.
- Choose rollout pattern: blue-green, canary, full replacement, or A/B based on change risk.
- Apply controlled changes in reversible order; record every change with timestamp, owner, and reason.
- Monitor health signals: quality, error rate, latency, token efficiency, user satisfaction, drift.
- Correlate incidents to recent version, prompt, config, model, or tool-permission changes.
- Decide and act: promote, pause, roll back, deprecate, or retire based on evidence and thresholds.
- Publish operational report: version status, rollout decision, health metrics, incidents, next actions.
Output
Operational report: agent name and active/candidate/fallback versions, selected rollout strategy, health summary, incident correlations, decision (promote/pause/rollback/deprecate/retire), next actions and owners.
References
Lifecycle state machine, health monitoring thresholds, deployment patterns, configuration rules, capacity planning, GitHub issue template, output format: agents/references/agentops-detail.md
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 Changed ca2be6e364a0
- 1mo ago First seen · 44 lines · 40 tokens per session scan A 4fa6ce709c48
agentops is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed 7d ago), licensed MIT. It adds 40 tokens to every session and 436 once invoked, about $0.0002 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 2026-08-31.
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