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 skills add gyanranjan/polyagent-skills --skill poc-spikegit clone --depth 1 https://github.com/gyanranjan/polyagent-skillsWrote 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/gyanranjan/polyagent-skills/poc-spike)<a href="https://agentmods.dev/skills/gyanranjan/polyagent-skills/poc-spike"><img src="https://agentmods.dev/badge/skills/gyanranjan/polyagent-skills/poc-spike/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gyanranjan/polyagent-skills/poc-spike"><img src="https://agentmods.dev/badge/skills/gyanranjan/polyagent-skills/poc-spike.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00068 | $0.01748 |
| Opus 5 | $0.00034 | $0.00874 |
| Sonnet 5 | $0.00014 | $0.00350 |
| Haiku 4.5 | $0.00007 | $0.00175 |
Grade A, and why
poc-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 10d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
POC / Spike
Purpose
Validate risky technical assumptions before committing to full implementation. Produce throwaway, minimal code that answers specific questions — then document the findings so the team can make informed go/no-go decisions.
A spike is NOT an implementation. It is an experiment with a findings report.
When to Use
- Gate 2 (Design) identified high-risk or uncertain technical items
- User asks to "spike", "prototype", "prove out", or "POC" something
- A technology/integration choice needs hands-on validation before commitment
- Performance or scalability assumptions need benchmarking
- User asks "will this even work?" or "can we actually do X?"
When NOT to Use
- Requirements are unclear (use
requirement-studyfirst) - No design exists yet (use
implementation-sketchfirst) - User wants production-quality code (that's Gate 4: Implementation)
- The risk is a business/market risk, not a technical one (use
idea-to-mvpinstead)
Inputs
Required:
- Risk items to investigate — specific technical questions to answer (ideally from Gate 2 risk register)
Optional:
- Implementation sketch — the design context for the risks
- Constraints — time box, technology restrictions, environment limits
- Success criteria — what constitutes a "validated" risk item
Process
Step 1: Define the Spike Scope
For each risk item, write a clear hypothesis and exit condition:
**Spike Item:** [SPIKE-NNN]
**Risk Source:** [REQ-NNN or risk register item]
**Hypothesis:** "We believe [technology/approach] can [achieve X] within [constraint Y]"
**Validation Method:** [What we'll build/test to prove or disprove]
**Exit Condition:** [Specific, measurable condition that answers the question]
**Time Box:** [Maximum time to spend — stop even if inconclusive]
Present the spike scope to the user:
"Here are the spike items I plan to investigate: [list]. Each has a time box of [N]. Does this scope look right?"
Do NOT proceed until the user confirms the scope.
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.
- 10d ago First seen · 202 lines · 68 tokens per session scan A b73721b793e8
poc-spike is a skill published in the GitHub repository gyanranjan/polyagent-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 68 tokens to every session and 1,748 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
spike
Throwaway experiments to validate an idea before build.
rapid-prototyping
Use this skill to build disposable, rapid proof-of-concept prototypes and spikes to test feasibility, validate API designs, or explore library capabilities before committing to a full production architecture.
spike
Time-boxed technical investigation to reduce uncertainty before committing to an approach.
assumption-mapper
Surfaces the assumptions a product or strategy depends on, classifies them by criticality and evidence quality, and produces a prioritized test plan. Use when: assumption mapping, identify assumptions, what could go wrong, leap-of-faith assumptions, riskiest assumption test, RAT, assumption inventory, validate before…
excavate
Digs beneath logic to inspect unchecked premises, rating each assumption by collapse severity.
qa-acceptance
Produce QA acceptance criteria and a manual validation plan for a feature change — golden path, edge cases, error states, performance limits, and explicit pass/fail evidence.