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 KhaledSaeed18/dotclaude --skill changeloggit clone --depth 1 https://github.com/KhaledSaeed18/dotclaudeWrote 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/khaledsaeed18/dotclaude/changelog)<a href="https://agentmods.dev/skills/khaledsaeed18/dotclaude/changelog"><img src="https://agentmods.dev/badge/skills/khaledsaeed18/dotclaude/changelog.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 | $0.00067 | $0.01112 |
| Opus 5 | $0.00034 | $0.00556 |
| Sonnet 5 | $0.00013 | $0.00222 |
| Haiku 4.5 | $0.00007 | $0.00111 |
Grade A, and why
changelog 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 7d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Produce a changelog from what actually shipped: the commits, merged PRs, and references between two points in history. Write it for the people who read releases (users, integrators), not as a raw git log dump.
Hard rules: never break these
- Base it on real history, never invent. Every entry must trace to a commit, PR, or issue. No speculative or aspirational items.
- Match the existing format. If
CHANGELOG.mdor a changelog tool already exists, follow its structure, headings, and conventions exactly. Don't impose a new style on an established file. - Never rewrite already-released sections. Add the new version on top; leave shipped entries untouched.
- Write for humans. Translate commits into user-facing outcomes. Drop pure-noise commits (formatting, CI tweaks, internal refactors) unless they affect users.
- Surface breaking changes prominently. They're the entries most likely to ruin someone's day if missed.
Step 1: Determine the range
- Last release to now:
git describe --tags --abbrev=0gives the latest tag; the range is<lastTag>..HEAD. - Between two releases:
<olderTag>..<newerTag>. - First release ever: use the full history (no lower bound).
- Honor an explicit range or version the user passed.
Step 2: Gather the raw material
- Commits in range:
git log <range> --no-merges --pretty=format:'%H %s'(and%bfor bodies, to catchBREAKING CHANGE:footers). - Merge commits / PR numbers:
git log <range> --mergesand(#123)references in subjects. - Parse Conventional Commit prefixes (
feat,fix,perf,refactor,docs, etc.), scopes, the!breaking marker, and issue/ticket references (#123,JIRA-456,CU-abc). - Capture authors if the project credits contributors.
Step 3: Detect the project's convention
Look before you write; adopt what's already in use:
- An existing
CHANGELOG.md/CHANGES/HISTORYfile → mirror its format (most follow Keep a Changelog). - Tooling config that owns the changelog, e.g.
conventional-changelog/standard-version,release-please, changesets (.changeset/), git-cliff (cliff.toml),towncrier(newsfragments/),auto,semantic-release. If one is configured, prefer driving it over hand-writing, and explain how to run it.
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.
- 7d ago First seen · 68 lines · 67 tokens per session scan A f1a64d44f1e4
changelog is a skill published in the GitHub repository KhaledSaeed18/dotclaude (5 stars, last pushed 6d ago), licensed MIT. It adds 67 tokens to every session and 1,112 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
pack-submit
Package one of this agent's own skills as a standalone community pack and submit it to the aeon registry as a PR.
shiplog
Recap of everything shipped since the last run - cross-repo PRs, security fixes, star deltas, and X traction, synthesized into a digest article and a ready-to-post shiplog in your voice.
no-mistakes
Validate committed feature-branch changes through the no-mistakes pipeline: intent, rebase, review, test, docs, lint, push, PR, and CI. Use when the user asks to run no-mistakes, ship safely, validate before pushing, or gate a change before it reaches upstream.
core-workflow
Detailed development workflow patterns, checklists, and standards. Auto-loads for complex tasks, planning, debugging, testing, or when explicit patterns are needed. Contains session protocols, git conventions, security checklists, testing strategy, and communication standards.
debug-systematic
Systematic 4-phase debugging methodology for complex, intermittent, or mysterious issues. Use when investigating bugs, race conditions, or unexplained failures.
auto-claude
Autonomous multi-agent coding with git worktree isolation, QA validation, and memory. Use for complex features requiring autonomous implementation.