debriefing-code-changes

A learning-focused review of changes made during an AI-assisted coding session. It examines a git diff or commit range and explains the actual files, architecture decisions, patterns, tradeoffs, caveats, and concepts involved.

In plain words
What is it for?
Use it after coding with AI to understand the changed code, identify the most valuable files to study, learn why design choices were made, review caveats, and test your understanding.
Why use it?
It turns an unfamiliar code change into an explanation grounded in the project’s files and functions instead of a generic programming lesson. It also checks understanding with a practical quiz and study notes.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/agent-engineer-master/skill-engineer/debriefing-code-changes
Any agent
npx skills add Agent-Engineer-Master/skill-engineer --skill debriefing-code-changes
Clone the repo
git clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineer

Made for: Claude Code, Codex.

Per session 128 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,213 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. Scan, not verified.
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 $0.00128 $0.01213
Opus 5 $0.00064 $0.00607
Sonnet 5 $0.00026 $0.00243
Haiku 4.5 $0.00013 $0.00121

Measured 2d ago against content hash b93f2ab77835, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

debriefing-code-changes scanned grade C with 1 finding 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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/collect_diff.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Hidden instructionshighPrompt injection

Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.

<!-- Built with Agent Engineer Master — get your own production-ready skill: www.agentengineermaster.com/skill-engineer -->
engineering/debriefing-code-changes/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Output Contract

Produces: a Code Change Debrief Markdown report grounded in a git diff or commit range, with: change summary, files-to-understand shortlist, per-change explanation, pattern/concept map, caveats, practical quiz, and optional durable notes.

Does NOT produce: generic CS lessons, ungrounded pattern spotting, code edits, refactors, or deploy approval.

Hands off to: the developer's learning loop — they read the debrief, answer the quiz, then decide whether to save notes, add tests, or request a separate code review/fix skill.


Process

Phase 1 — Collect the actual change set

  1. Ask for or infer the target range: unstaged diff, staged diff, last commit, branch diff, or explicit commit range.
  2. Prefer git facts over chat memory. Run scripts/collect_diff.py or equivalent git commands to capture: changed files, diff stats, commits, file excerpts, and touched functions/classes where available.
  3. If the diff is too large, shortlist the 3–7 files with the highest learning value: core logic, new abstractions, boundary code, data model changes, tests, and security-sensitive paths.
  4. If no diff or commit range exists, stop and ask for one. Do not debrief from the chat transcript alone.

Why: the lesson lives in code the developer now owns. Chat transcripts are noisy and often explain intentions that never landed in the repository.

Read references/debrief-rubric.md before writing the report.


Phase 2 — Explain intent and structure per non-trivial change

For each selected change, produce this block:

### [File or subsystem]
What changed: [plain-English summary]
Why it was shaped this way: [architecture/design rationale inferred from the code]
Key code to read: [file:function or file:line when available]
Pattern or concept: [only if visibly present in the code]
Tradeoff accepted: [what this design makes easier vs harder]
One thing to watch: [edge case, coupling risk, missing test, performance/security caveat]

Read the full file on GitHub · 109 lines

Files

What ships with it

6 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.

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. 2d ago First seen · 109 lines · 128 tokens per session scan C b93f2ab77835

Subscribe to this mod's changes

debriefing-code-changes is a skill published in the GitHub repository Agent-Engineer-Master/skill-engineer (7 stars, last pushed 1mo ago), licensed MIT. It adds 128 tokens to every session and 1,213 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.