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 vasilyu1983/AI-Agents-public --skill ai-coding-agents-observability-evalsgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-coding-agents-observability-evals)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-coding-agents-observability-evals"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-observability-evals/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/vasilyu1983/ai-agents-public/ai-coding-agents-observability-evals"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-observability-evals.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 | $0.00042 | $0.05006 |
| Opus 5 | $0.00021 | $0.02503 |
| Sonnet 5 | $0.00008 | $0.01001 |
| Haiku 4.5 | $0.00004 | $0.00501 |
Grade A, and why
ai-coding-agents-observability-evals 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 12d 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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Coding Agents Observability And Evals
Use this skill to design or review the feedback loop around a coding-agent runtime: traces, replayable transcripts, eval packs, regression gates, tool-call grading, latency and cost accounting, and production failure triage.
This skill covers how you operate a coding-agent product after the core runtime exists. It does not replace the runtime skills themselves.
ASCII Flow
agent session
|
v
trace events
prompts + model turns + tool calls + permissions + file diffs + costs
|
v
replayable transcript
stable IDs + redaction + source/runtime correlation
|
v
eval pack
golden tasks + graders + regression gates + cost/latency budgets
|
v
release decision
pass | investigate | rollback | update eval coverage
Quick Reference
| Question | Read | Outcome |
|---|---|---|
| What should the trace and telemetry model include? | references/trace-and-telemetry-model.md |
Durable trace schema, session correlation, event stages, and replay boundaries |
| How should evals, regressions, and cost controls work? | references/evals-regression-and-cost-ops.md |
Golden tasks, iterative self-extension packs, trajectory scorecards, and cost-aware release gates |
| How do I use the eval/trace substrate to improve the harness itself? | references/harness-self-evolution.md |
Closed-loop harness evolution: three observability pillars, falsifiable-contract edits, attribution |
| How does OpenAI Codex combine rollout replay, SQLite state, doctor reports, and telemetry? | references/openai-codex-rollout-doctor-telemetry.md |
Replay artifacts, rebuildable state indexes, redacted diagnostics, W3C traces, token metrics |
| How does Codex wire OTel exporters and what analytics events exist? | references/openai-codex-otel-config.md |
OtelSettings TOML schema, exporter selection, W3C tracestate, contrast with proprietary analytics events |
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.
- agents/openai.yaml 400 B
- assets/templates/golden-task.schema.json 6.9 KB
- data/sources.json 5.6 KB
- learnings.consolidated.md 612 B
- learnings.md 1.5 KB
- references/evals-regression-and-cost-ops.md 6.3 KB
- references/harness-self-evolution.md 5.3 KB
- references/openai-codex-otel-config.md 6.3 KB
- references/openai-codex-rollout-doctor-telemetry.md 2.7 KB
- references/recovery-trace-events.md 7.1 KB
- references/trace-and-telemetry-model.md 3.3 KB
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.
- 12d ago First seen · 265 lines · 42 tokens per session scan A eff0ec5bbec0
ai-coding-agents-observability-evals is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 42 tokens to every session and 5,006 once invoked, about $0.0002 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.
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