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 5.5 | $0.00027 | $0.00699 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 7mo ago), licensed MIT. It adds 68 tokens to every session and 1,748 once invoked, about $0.0003 per session on Opus 5.5. 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.
excavate
Digs beneath logic to inspect unchecked premises, rating each assumption by collapse severity. Now with sensitivity surfaces, counterfactual worlds, boundary detection integration, and mathematical depth metrics.
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…
spike
Time-boxed technical investigation to reduce uncertainty before committing to an approach.
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.