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 agentmods add skills/drag88/claude-dev-framework/retronpx skills add drag88/claude-dev-framework --skill retrogit clone --depth 1 https://github.com/drag88/claude-dev-frameworkWrote 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/drag88/claude-dev-framework/retro)<a href="https://agentmods.dev/skills/drag88/claude-dev-framework/retro"><img src="https://agentmods.dev/badge/skills/drag88/claude-dev-framework/retro.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 | $0.00021 | $0.01990 |
| Opus 5 | $0.00010 | $0.00995 |
| Sonnet 5 | $0.00004 | $0.00398 |
| Haiku 4.5 | $0.00002 | $0.00199 |
Grade A, and why
retro 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 3d 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/retro — Engineering Retrospective
Generates a comprehensive engineering retrospective from git history. Surfaces velocity, patterns, and quality signals that are invisible in day-to-day work.
When to Activate
- User types
/retro - User asks for "weekly metrics", "engineering stats", "how productive was I", "shipping velocity"
Arguments
/retro— default: last 7 days/retro 24h— last 24 hours/retro 14d— last 14 days/retro 30d— last 30 days/retro compare— compare current window vs prior same-length window/retro compare 14d— compare with explicit window
Validate argument format: number + d/h/w, or compare optionally followed by window. If invalid, show usage and stop.
Instructions
Step 1: Gather Raw Data
Fetch origin first, then run the remaining git commands.
git fetch origin --quiet
# 1. Commits with stats
git log origin/main --since="<window>" --format="%H|%ai|%s" --shortstat
# 2. Per-commit test vs production LOC (test/|spec/|__tests__/ = test files)
git log origin/main --since="<window>" --format="COMMIT:%H" --numstat
# 3. Timestamps for session detection
git log origin/main --since="<window>" --format="%at|%ai|%s" | sort -n
# 4. Hotspot analysis
git log origin/main --since="<window>" --format="" --name-only | grep -v '^$' | sort | uniq -c | sort -rn
# 5. PR numbers from commit messages
git log origin/main --since="<window>" --format="%s" | grep -oE '#[0-9]+' | sed 's/^#//' | sort -n | uniq | sed 's/^/#/'
Step 2: Compute Metrics
Present as summary table:
| Metric | Value |
|---|---|
| Commits to main | N |
| PRs merged | N |
| Total insertions | N |
| Total deletions | N |
| Net LOC added | N |
| Test LOC (insertions) | N |
| Test LOC ratio | N% |
| Active days | N |
| Detected sessions | N |
| Avg LOC/session-hour | N |
Step 3: Commit Time Distribution
Hourly histogram with bar chart:
Hour Commits ████████████████
00: 4 ████
07: 5 █████
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.
- 3d ago First seen · 234 lines · 21 tokens per session scan A 8eb83a9fe8f3
retro is a skill published in the GitHub repository drag88/claude-dev-framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 1,990 once invoked, about $0.0001 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…