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/rmjosea/agentic-sdlc-kit/plannpx skills add rmjosea/agentic-sdlc-kit --skill plangit clone --depth 1 https://github.com/rmjosea/agentic-sdlc-kitWhat 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 | $0.00095 | $0.00611 |
| Opus 5 | $0.00048 | $0.00305 |
| Sonnet 5 | $0.00019 | $0.00122 |
| Haiku 4.5 | $0.00010 | $0.00061 |
Grade A, and why
plan 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 2d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan an Implementation
Convert approved product behavior into a technically coherent design. Do not modify production code.
Discover
- Read only the authoritative source intent and relevant repository guidance.
- Inspect the codebase, tests, dependencies, and established conventions.
- Identify constraints, reusable components, trust boundaries, and unknowns.
- Separate repository facts, confirmed constraints, assumptions, and choices.
Resolve decisions conversationally
For every material uncertainty, apply the human decision protocol in
AGENTS.md:
- ask one clear, self-contained question at a time;
- offer distinct alternatives with consequences;
- mark and justify the recommended option;
- update the confirmed design after each answer.
Ask when a choice materially affects architecture, data, APIs, security, cost, operability, compatibility, reversibility, or task structure. Decide trivial, reversible, repository-standard details directly and record the rationale.
Do not write the final plan until material decisions are resolved and the user has confirmed the proposed technical direction.
Design
Cover only what the change needs:
- components and responsibilities;
- interfaces and integration points;
- data ownership, model, and migration;
- security and privacy boundaries;
- failure handling and recovery;
- compatibility and rollout;
- observability;
- verification strategy.
Create a separate technical artifact only when several tasks share it, it needs
independent review, or moving it out materially reduces task context. Possible
artifacts include architecture.md, data-model.md, api-contract.md,
migration.md, and test-strategy.md.
Use assets/plan.template.md and write temporary
planning artifacts under .work/<feature>/. Keep the plan draft while a
material decision or user confirmation is pending; set it to approved only
after the user confirms the complete technical direction.
Quality gate
What ships with it
1 file 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.
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.
- 2d ago First seen · 73 lines · 95 tokens per session scan A ae7783447dc7
plan is a skill published in the GitHub repository rmjosea/agentic-sdlc-kit (2 stars, last pushed 1mo ago), licensed MIT. It adds 95 tokens to every session and 611 once invoked, about $0.0005 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…