evlog is a TypeScript-first logging and observability project built around wide events and structured errors across runtimes. It is intended to help developers record application behavior and diagnose failures.
Borrowing it
Nothing to install: this file belongs to HugoRCD/evlog. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/HugoRCD/evlog/main/.agents/skills/write-evlog-content/SKILL.mdgit clone --depth 1 https://github.com/HugoRCD/evlogWrote 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/hugorcd/evlog/write-evlog-content)<a href="https://agentmods.dev/skills/hugorcd/evlog/write-evlog-content"><img src="https://agentmods.dev/badge/skills/hugorcd/evlog/write-evlog-content/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/hugorcd/evlog/write-evlog-content"><img src="https://agentmods.dev/badge/skills/hugorcd/evlog/write-evlog-content.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 | $0.00121 | $0.02436 |
| Opus 5.5 | $0.00048 | $0.00974 |
| Sonnet 5.5 | $0.00024 | $0.00487 |
| Haiku 4.5 | $0.00012 | $0.00244 |
Grade A, and why
write-evlog-content 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 2mo 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing evlog content
Everything needed to draft or judge evlog prose. Two roles use this skill and they must not be merged.
Review produces findings and a verdict. It never rewrites, never softens, never proposes wording. Rewrite applies findings. It touches only what a finding names, and cites the rule or tell id for every change.
Splitting them is what keeps the loop honest. A reviewer that can rewrite talks itself into changes it cannot justify, and a rewriter that can re-judge its own output always passes.
Structure
references/
voice.md the voice and the five tests. Load first, always
rules/ atomic rules, one file per surface group
universal.md every surface
docs.md apps/docs/content
blog.md blog posts
landing.md 0.landing.md and other marketing surfaces
machine.md skills and AGENTS.md, the surfaces an agent acts on
ai-tells.md the tell corpus, each tell with its legitimate twin
terminology.md the names evlog gave its own parts (U-15)
landscape/ what pino, winston, consola, and OpenTelemetry actually do (U-12)
surfaces/ what each surface owes its reader
docs.md blog.md landing.md readme.md skill.md agents.md changeset.md
samples.md evlog pages that read right, and why. What the tells must not flag
corrections.md accumulated lessons from rejected rewrites. Grows over time
Load voice.md first. Then the rule file for the surface, ai-tells.md when reviewing, and the matching surfaces/ file when drafting. Open terminology.md when a U-15 candidate is in play and the relevant landscape/ dossier before writing any sentence that names another logger. Do not load everything.
The corpus
Everything evlog ships as prose, on both sides of the line:
| Read by | Surfaces | What decides quality |
|---|---|---|
| People | docs pages, the landing, blog posts, the package READMEs | Whether the reader can act, and whether they believe the page |
| Agents | .agents/skills/, skills/, the AGENTS.md files |
Whether an agent does the right thing having read only this |
What ships with it
22 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/ai-tells.md 12 KB
- references/corrections.md 9.9 KB
- references/landscape/consola.md 2.4 KB
- references/landscape/opentelemetry.md 1.6 KB
- references/landscape/pino.md 1.9 KB
- references/landscape/README.md 1.6 KB
- references/landscape/winston.md 1.6 KB
- references/rules/blog.md 4.1 KB
- references/rules/docs.md 5.3 KB
- references/rules/landing.md 2.9 KB
- references/rules/machine.md 4.1 KB
- references/rules/universal.md 9.6 KB
- references/samples.md 3.0 KB
- references/surfaces/agents.md 1.9 KB
- references/surfaces/blog.md 2.7 KB
- references/surfaces/changeset.md 1.3 KB
- references/surfaces/docs.md 3.0 KB
- references/surfaces/landing.md 2.3 KB
- references/surfaces/readme.md 1.1 KB
- references/surfaces/skill.md 2.0 KB
- references/terminology.md 2.8 KB
- references/voice.md 4.3 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.
- 2mo ago First seen · 136 lines · 121 tokens per session scan A 63e9e66a9252
write-evlog-content is a skill published in the GitHub repository HugoRCD/evlog (1,903 stars, last pushed 12d ago), licensed MIT. It adds 121 tokens to every session and 2,436 once invoked, about $0.0005 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.
write-evlog-content compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| write-evlog-contentHugoRCD/evlog | Skill | 1.9k | 12d ago | MIT | A | 121 | Claude Code, Codex |
| gh-issueapache/skywalking-banyandb | Skill | 355 | today | Apache-2.0 | A | 91 | Claude Code |
| gh-pull-requestapache/skywalking-banyandb | Skill | 355 | today | Apache-2.0 | A | 37 | Claude Code |
| vendor-updateapache/skywalking-banyandb | Skill | 355 | today | Apache-2.0 | A | 95 | Claude Code |
| bydbqlapache/skywalking-banyandb | Skill | 355 | today | Apache-2.0 | A | 71 | Claude Code |
| frontmcp-observabilityagentfront/frontmcp | Skill | 148 | yesterday | Apache-2.0 | A | 196 | Claude Code, Codex |
| compilingapache/skywalking-banyandb | Skill | 355 | today | Apache-2.0 | A | 31 | Claude Code |