dot-ai-changelog-fragment

dot-ai-changelog-fragment is a skill for Claude Code from vfarcic/dot-ai. It costs 30 tokens per session (1,239 once invoked), scanned A, original, MIT.

A release-note fragment tool for Towncrier, a system that collects small changelog files and combines them into release notes. It is used during the project's pull-request completion workflow.

In plain words
What is it for?
Use it to read a product-requirements document, choose the appropriate changelog type, and create the release-note fragment for the completed work.
Why use it?
It prevents completed work from reaching a release without a matching, reviewed changelog entry.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Good fit Use it to read a product-requirements document, choose the appropriate changelog type, and create the release-note fragment for the completed work.

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Install with agentmods
npx agentmods add skills/vfarcic/dot-ai/dot-ai-changelog-fragment
Install

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.

Any agent
npx skills add vfarcic/dot-ai --skill dot-ai-changelog-fragment
Clone the repo
git clone --depth 1 https://github.com/vfarcic/dot-ai

Made for: Claude Code.

Wrote this? Show the measurements

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README.md
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Your own site
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Your own site · 80×15
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Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,239 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 81
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • high Privilege Escalation · line 81
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00030 $0.01239
Opus 5 $0.00015 $0.00620
Sonnet 5 $0.00006 $0.00248
Haiku 4.5 $0.00003 $0.00124

Measured 12d ago against content hash 0e8d21d61a78, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

dot-ai-changelog-fragment 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.

.claude/skills/dot-ai-changelog-fragment/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Create Changelog Fragment

Create a towncrier changelog fragment for release notes when completing PRD work. This should be included in the PR so the fragment is reviewed along with the code changes.

Workflow

Step 1: Identify the PRD

If not already known from context, ask: "Which PRD should I create release notes for?"

Look for:

  • PRD mentioned in recent conversation
  • PRD referenced in current branch name (e.g., feature/prd-320-*)
  • PRD file path provided by user

Step 2: Read the PRD Thoroughly

Read the entire PRD file to extract:

  • Problem Statement: What user pain point was solved, why it mattered
  • Solution Overview: What the feature does, how it works
  • User Impact: Specific benefits, what users can now do
  • Key Capabilities: Individual features, options, or modes added
  • Technical Details: Configuration options, environment variables, commands
  • Documentation Updates: Which docs were added or updated (check Milestones section)

Step 3: Determine Fragment Type

Read pyproject.toml to see the available fragment types. Each [[tool.towncrier.type]] section has:

  • A comment above it describing when to use that type
  • A directory field (the type identifier used in the filename, e.g., feature for .feature.md)

First, ask the breaking question — before matching the change to any other type. Determine whether this change is breaking as this project defines it: does it break compatibility with a prior release, or otherwise require existing users to take action to keep working? Read the project's breaking-type comment in pyproject.toml for the project-specific definition — it may be broader than an API/CLI change (for example a wire-protocol, on-disk-format, or other cross-process/compatibility contract, including a change that stays structurally compatible but shifts meaning). When the change matches that definition — or you are unsure and it plausibly could — prefer the breaking type. Under-classifying a breaking change is costly: it ships with the wrong version bump and with no breaking-change entry to warn users.

Read the full file on GitHub · 103 lines

Changes

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.

  1. 12d ago First seen · 103 lines · 30 tokens per session scan A 0e8d21d61a78

Subscribe to this mod's changes

dot-ai-changelog-fragment is a skill published in the GitHub repository vfarcic/dot-ai (336 stars, last pushed 5d ago), licensed MIT. It adds 30 tokens to every session and 1,239 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.