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 denysdovhan/agents --skill agent-loggit clone --depth 1 https://github.com/denysdovhan/agentsWrote 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/denysdovhan/agents/agent-log)<a href="https://agentmods.dev/skills/denysdovhan/agents/agent-log"><img src="https://agentmods.dev/badge/skills/denysdovhan/agents/agent-log/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/denysdovhan/agents/agent-log"><img src="https://agentmods.dev/badge/skills/denysdovhan/agents/agent-log.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 | $0.00095 | $0.02074 |
| Opus 5.5 | $0.00038 | $0.00830 |
| Sonnet 5.5 | $0.00019 | $0.00415 |
| Haiku 4.5 | $0.00010 | $0.00207 |
Grade A, and why
agent-log 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Log
Maintain .agents/log/ — a version-controlled journal of product and engineering decisions, tradeoffs, and directions, co-owned by the developer and agents. Entries capture why — the reasoning that code and git history cannot recover.
Layout
.agents/log/index.md— index of all entries.agents/log/YYYY-MM-DD-slug.md— one entry per feature or standalone decision
Initialize .agents/log/ only when the user explicitly asks — by invoking this skill or directly requesting an agent log. If .agents/log/ is absent otherwise, do not create it, even when a plan is approved. When initializing, create .agents/log/index.md with the table header from Index Format; create an entry only if the current task records a decision.
Entry Format
---
title: Human-readable decision or feature title
date: YYYY-MM-DD
status: wip | done
related_paths:
- src/feature-area/
- src/shared/specific-file.ts
---
# Human-readable decision or feature title
## Background
Context needed to understand the problem. Link related entries as
[title](YYYY-MM-DD-other-entry.md).
## Problem
What is being solved and why now.
## Questions & Answers
Clarifying questions asked and the user's answers. Open questions stay
here until resolved.
## Decision
The chosen design. Concrete: file paths, type signatures, data shapes.
Mark examples with ✅ (do) and ❌ (don't) — the canonical notation for all
entries. Use Mermaid diagrams only when they clarify the decision.
## Tradeoffs & Alternatives
Rejected options and why. This section prevents re-litigating decisions
later.
## Implementation Plan
Phases or steps, when the entry starts as a plan draft.
## Verification
How success is checked, as a checklist:
- [ ] Tests, behaviors, or measurable criteria
- [ ] Manual checks that need a human
## Implementation Notes
Dated notes appended during and after implementation: deviations from
the plan, discoveries, test outcomes.
Make the level-one heading the first content after frontmatter and match its text
exactly to the frontmatter title. Use only one level-one heading. Omit sections
that have no content. Keep entries short and decision-focused — do not document
what the code or git history already states.
What ships with it
3 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.
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 · 193 lines · 95 tokens per session scan A 64e91efba1bd
agent-log is a skill published in the GitHub repository denysdovhan/agents (21 stars, last pushed today), licensed MIT. It adds 95 tokens to every session and 2,074 once invoked, about $0.0004 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.
Other skills, from other repositories
brain-docs
Set up and maintain a "project brain" — a brain/ folder holding three living documents (decisions.md, flow.md, todo.md) that give a project persistent memory across chat sessions. Use this skill whenever the user wants a decision log, architecture/flow notes, project memory, living documentation, or notes that survive…
capture
A session-recap and checkpoint tool for Tracework memory, used when a work session ends or the user asks to save progress.
golang-security
Security best practices and vulnerability prevention for Golang. Covers injection (SQL, command, XSS), cryptography, filesystem safety, network security, cookies, secrets management, memory safety, and logging. Apply when writing, reviewing, or auditing Go code for security, or when working on any risky code involving…
ai-agents-architect
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
run-memory
Shared step of the marketing playbooks (analyze-performance, run-paid-campaigns, research-competitors, email-lifecycle): reads the last run's record, checks whether its recommendations were done and whether they worked, and saves a short record of this run for the next one. Loaded by those playbooks; not for direct…
company-brain
Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so an agent can answer on your team's behalf. Team-scope sibling to second-brain — personal notes go there, not here. Modes — capture, compile (wiki pages + INDEX.md), query (trust-weighted, saved to outputs/)…