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 KryptosAI/agent-observability --skill agent-obs-opencodegit clone --depth 1 https://github.com/KryptosAI/agent-observabilityWrote 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/kryptosai/agent-observability/agent-obs-opencode)<a href="https://agentmods.dev/skills/kryptosai/agent-observability/agent-obs-opencode"><img src="https://agentmods.dev/badge/skills/kryptosai/agent-observability/agent-obs-opencode/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/kryptosai/agent-observability/agent-obs-opencode"><img src="https://agentmods.dev/badge/skills/kryptosai/agent-observability/agent-obs-opencode.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 | $0.00085 | $0.02723 |
| Opus 5.5 | $0.00034 | $0.01089 |
| Sonnet 5.5 | $0.00017 | $0.00545 |
| Haiku 4.5 | $0.00009 | $0.00272 |
Grade A, and why
agent-obs-opencode 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-obs Self-Reporting
You are an observed agent. The agent-obs MCP server is a local flight recorder
for everything you do: tool calls, errors, tokens, cost, and a final A-F grade.
It only works if you report into it. This skill defines the reporting protocol.
Follow it in every task, in every session, without being asked.
The Protocol (four tools, three phases)
Phase 1 — Start: call start_session FIRST
- At the very beginning of every task — before Read, before Bash, before
anything else — call the
start_sessiontool on theagent-obsserver. - Pass:
description(required): one sentence describing the task, e.g."Fix login redirect bug in auth middleware".agentType: your client, e.g."opencode","claude-code","cursor","codex".model: your model id, e.g."deepseek/deepseek-v4-pro".
- The result contains a session
id. Save it. Every subsequentlog_tool_calland the finalend_sessionneeds this exactsessionId. - One task = one session. If the user pivots to a brand-new task in the same conversation, end the current session and start a new one.
Example:
start_session({
"description": "Refactor payment webhook handler and add tests",
"agentType": "opencode",
"model": "deepseek/deepseek-v4-pro"
})
→ { "id": "a1b2c3d4" } // keep this sessionId
Phase 2 — Log: call log_tool_call after EVERY tool call
- Immediately after each tool call completes (Read, Write, Edit, Bash, Glob,
Grep, Task, WebFetch, TodoWrite, any MCP tool — ALL of them), call
log_tool_callwith:sessionId(required): the id fromstart_session.toolName(required): the tool you just used, e.g."Read","Bash","Edit".status(required):"success"if the tool call worked,"error"if it failed, errored, or was rejected.toolServer: where the tool lives, e.g."builtin","playwright","cloudflare-bindings".input: a compact object of the arguments, e.g.{ "filePath": "src/auth.ts" }. Redact secrets — never log tokens, keys, or passwords.outputSummary: one line describing what happened, e.g."Read 120 lines of auth middleware"or"npm test: 42 passed".durationMs: approximate wall-clock duration if known.errorMessage: the error text whenstatusis"error".
- NEVER skip
log_tool_call— even for fast operations. Every tool call must be logged. A one-line Glob counts. A 2ms Read counts. If you called a tool, you log it. No batching several calls into one log entry, no "it was trivial" exceptions. - Log failures too. An errored Bash command logged with
status: "error"is exactly the signal the grading system needs. - The only calls you do NOT log are the
agent-obstools themselves (start_session,log_tool_call,end_session,get_last_session). Logging the logger would recurse forever.
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 · 256 lines · 85 tokens per session scan A 305972560401
agent-obs-opencode is a skill published in the GitHub repository KryptosAI/agent-observability (2 stars, last pushed 2mo ago), licensed MIT. It adds 85 tokens to every session and 2,723 once invoked, about $0.0003 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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