Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/neverinfamous/mysql-mcp/agent-observability-experiment-py-bootstrap)<a href="https://agentmods.dev/skills/neverinfamous/mysql-mcp/agent-observability-experiment-py-bootstrap"><img src="https://agentmods.dev/badge/skills/neverinfamous/mysql-mcp/agent-observability-experiment-py-bootstrap.svg" alt="Measured on agentmods" 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 | $0.00103 | $0.14274 |
| Opus 5 | $0.00051 | $0.07137 |
| Sonnet 5 | $0.00021 | $0.02855 |
| Haiku 4.5 | $0.00010 | $0.01427 |
Grade A, and why
agent-observability-experiment-py-bootstrap scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| `requests.post`, `httpx.post` | The skill produces SDK-only code. Direct HTTP calls bypass the SDK's lazy creation, push-diff, and bulk-threshold handling. | How it starts
The opening of the file, as written. The whole thing — 639 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backend
Detection — At the start of every invocation, before taking any action, determine which backend (if any) to use for the startup beacon call:
- If the user passed
--backend pupanywhere in their invocation → use pup mode for the beacon. Skip steps 2–3. - Check whether MCP tools are present in your active tool list. The canonical signal is whether
mcp__datadog-llmo-mcp__list_llmobs_evalsappears in your available tools. If present → use MCP mode. - If MCP tools are absent → check whether
pupis executable viapup --version. If responding → use pup mode. - If neither is available → skip the beacon silently and proceed to codegen. This skill is primarily local (introspection + file emission) and does not require backend access; the beacon is for usage attribution only.
Invocation ID: At the very start of each invocation, before any MCP/pup call, generate an 8-character hex invocation ID (e.g., 3a9f1c2b). Keep it constant for the entire invocation.
Intent tagging: On every MCP tool call, prefix telemetry.intent with skill:agent-observability-experiment-py-bootstrap[<inv_id>] — followed by a description of why the tool is being called. On the first MCP tool call only (the startup beacon below), use skill:agent-observability-experiment-py-bootstrap:start[<inv_id>] — instead (note the :start suffix).
Startup beacon: Immediately after parsing arguments (workflow step 1, before dataset resolution in step 2), issue exactly one beacon call to register skill usage and validate backend connectivity. This is fire-and-forget — surface any error as a one-line Note: to the user but do not block codegen.
- MCP mode: call
mcp__datadog-llmo-mcp__list_llmobs_evalswithtelemetry.intent = "skill:agent-observability-experiment-py-bootstrap:start[<inv_id>] — Skill startup: register usage and verify Datadog connectivity". Discard the response payload; the call's purpose is the telemetry tag. - pup mode: run
pup llm-obs evals list --limit 1via Bash. Pup carries its own telemetry; no intent prefix needed. - No backend: print one line
(Telemetry beacon skipped — no Datadog backend detected; this is informational only and does not affect codegen.)and proceed.
The beacon must not fail the skill. If the call errors (auth, network, etc.), surface a one-line note and continue.
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/evaluator-styles/class.md 1.8 KB
- references/evaluator-styles/function.md 1.8 KB
- references/evaluator-styles/remote.md 1.9 KB
- references/providers/anthropic.md 1.6 KB
- references/providers/bedrock.md 2.3 KB
- references/providers/gemini.md 2.0 KB
- references/providers/langchain.md 2.4 KB
- references/providers/litellm.md 1.9 KB
- references/providers/llamaindex.md 2.3 KB
- references/providers/openai.md 1.8 KB
- scripts/env_setup_template.py 3.4 KB runs code
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.
- 8d ago First seen · 639 lines · 103 tokens per session scan A 975bcc334557
agent-observability-experiment-py-bootstrap is a skill published in the GitHub repository neverinfamous/mysql-mcp (10 stars, last pushed yesterday), licensed MIT. It adds 103 tokens to every session and 14,274 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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