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/codagent-ai/agent-skills/proposenpx skills add Codagent-AI/agent-skills --skill proposegit clone --depth 1 https://github.com/Codagent-AI/agent-skillsWhat 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 | $0.00063 | $0.00668 |
| Opus 5 | $0.00032 | $0.00334 |
| Sonnet 5 | $0.00013 | $0.00134 |
| Haiku 4.5 | $0.00006 | $0.00067 |
Grade A, and why
propose 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Propose
Evaluate an idea honestly, then write proposal.md when it is worth pursuing. The proposal explains
the motivation deeply, bounds the change at a high level, and sketches only enough technical approach
to establish feasibility and expose structural risk. Detailed behavior belongs in specifications;
detailed architecture belongs in design.md.
Process
1. Understand and research
Use codagent:ask-questions when the problem, audience, desired outcome, success criteria, constraints,
or scope boundaries are unclear. Do not draft from a rough idea when an answer would materially change
the proposal.
Ground the evaluation in available evidence:
- read related specifications and relevant code to understand current behavior, architecture, and existing patterns;
- investigate prior attempts, available tools, and credible alternatives;
- use web research when current external practices, products, or known pitfalls matter.
2. Evaluate
Assess the problem's significance, alternatives to building, opportunity cost, maintenance burden, and fit with the existing system. Give a direct verdict: go, go with caveats, or no-go, with the reasons and any condition that would change it. A no-go produces no proposal unless the user decides to proceed after discussing the trade-offs.
3. Establish the high-level approach
For a viable idea, identify the minimum useful scope, affected capabilities, architecture fit, major technical decisions, and material risks. When real alternatives exist, recommend one and explain the important trade-off; ask the user only when the choice changes product scope or direction. Decide low-risk implementation defaults from repository context.
Keep this intentionally lighter than design. Use a diagram or comparison only when it clarifies an important relationship or decision.
4. Approve and write
Present the recommendation, scope, and approach for user approval before writing. Include any consequential assumptions or defaults you selected so the user can correct them.
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 · 89 lines · 63 tokens per session scan A 59f9f80fe7de
propose is a skill published in the GitHub repository Codagent-AI/agent-skills (30 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 668 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 2026-08-30.
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…