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/seanrreid/rad_framework/wrapnpx skills add seanrreid/RAD_framework --skill wrapgit clone --depth 1 https://github.com/seanrreid/RAD_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/seanrreid/rad_framework/wrap)<a href="https://agentmods.dev/skills/seanrreid/rad_framework/wrap"><img src="https://agentmods.dev/badge/skills/seanrreid/rad_framework/wrap.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 | $0.00052 | $0.01198 |
| Opus 5 | $0.00026 | $0.00599 |
| Sonnet 5 | $0.00010 | $0.00240 |
| Haiku 4.5 | $0.00005 | $0.00120 |
Grade A, and why
wrap 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Wrap-Up
Capture what happened so the next session starts clean. No project-specific files — this works on any RAD project.
Steps
1. Gather session changes
git branch --show-current
git log --oneline @{u}.. 2>/dev/null || git log --oneline -10
git status --short
Collect the commits made this session, the current branch, and any uncommitted changes.
2. Update plan status if it changed
If the session advanced a plan, update its Status: on the work branch and push
so the board (which reads rad/ branch tips) stays current:
- Delivery started this session →
Status: in-progress - Deliver PR opened this session →
Status: review(if your project uses it) and note the PR - Plan still being drafted → leave
pending-review
# On the rad/ work branch:
git add .agents/plans/<feature>.md
git commit -m "wrap(<feature>): session status update"
git push origin "rad/<feature>"
scripts/rad-label.sh <issue-number> <status> # omit if there is no issue
3. Append a dated progress note
If meaningful work happened on a plan but its status didn't change, append a dated
line to the plan's ## Notes section (create the section if absent) so the next
session has continuity:
- {date}: {one-line summary of what was done and what's next}
4. Reconcile plan vs. actual (delivery sessions only)
If on a rad/ branch with an execution log under .agents/logs/, produce a brief
reconciliation note. Skip silently if there's no log.
# Find the execution log for this feature
ls .agents/logs/[feature-name]-*.md 2>/dev/null | tail -1
Check three things:
ACs covered: For each numbered AC in the plan, was it cited in a completed task commit? Note any that were deferred or skipped.
Concerns flagged: Were any tasks marked done_with_concerns? List each concern
one-line so the architect sees them in the session summary without digging into the log.
Deferred items: Anything in ## Non-Goals or the wave plan that was explicitly
left for a follow-up? Name it so the next session has a standing start.
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 · 154 lines · 52 tokens per session scan A 01a120d31a32
wrap is a skill published in the GitHub repository seanrreid/RAD_framework (5 stars, last pushed 7d ago), licensed MIT. It adds 52 tokens to every session and 1,198 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
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…