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/leifericf/agentic-sdk/run-spikenpx skills add leifericf/agentic-sdk --skill run-spikegit clone --depth 1 https://github.com/leifericf/agentic-sdkWrote 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/leifericf/agentic-sdk/run-spike)<a href="https://agentmods.dev/skills/leifericf/agentic-sdk/run-spike"><img src="https://agentmods.dev/badge/skills/leifericf/agentic-sdk/run-spike.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 | $0.00000 | $0.01670 |
| Opus 5 | $0.00000 | $0.00835 |
| Sonnet 5 | $0.00000 | $0.00334 |
| Haiku 4.5 | $0.00000 | $0.00167 |
Grade A, and why
run-spike 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 4d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
run-spike
A spike buys knowledge, not a feature. Run one when a load-bearing unknown sits in front of real work and you cannot plan past it honestly. Front-load the biggest unknowns, the ones at the seams: the native edge (a C ABI, NIF, or foreign-function boundary that may leak or crash under churn), a real-time budget a renderer or audio path must hit, a concurrency model the single-threaded path hides.
A spike learns; a feature ships. When the answer is known and the work
is to build the planned thing, that is implement-change. When the
work is to break existing code, that is adversarial-test. A spike has
no user; its deliverable is a decision plus, sometimes, a harness worth
keeping.
Procedure
The discipline, in order. Do not skip steps.
1. Name the unknown and the question
Write down, in one or two sentences each:
- The load-bearing unknown. The thing that, if it goes the wrong way, changes the plan.
- The question the spike must answer, phrased so the answer is a fact, not an opinion. "Does a hundred thousand native-handle create and free cycles return allocation to zero?" not "Is the native edge good enough?" A fuzzy question produces a fuzzy decision.
- The decision the answer unblocks: which planned work waits on this, and what the go, no-go, and mitigate branches each mean for it.
State all three to the maintainer before building anything. The spike is scoped wrong if you cannot.
2. Time-box it
Set a budget up front: hours, or a fixed number of harness iterations. A spike runs until it answers the question or the box closes, whichever comes first. A box that closes with no clear answer is itself a finding: the seam is harder to characterize than expected, and that goes in the write-up. Do not let a spike slide into building the feature because the harness started to look real.
3. Build a standalone, throwaway harness
The harness is scaffolding, not product. Build it to be discarded (step 7 decides if any earns a place). Keep it isolated: its own namespace, module, or file under a spike path, no edits to the modules it probes, scratch state under a tmp dir.
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.
- 4d ago First seen · 157 lines · 0 tokens per session scan A 9dc777d8be6e
run-spike is a skill published in the GitHub repository leifericf/agentic-sdk (5 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,670 tokens. 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
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brainstorming
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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…