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 Lum1104/bet-on-it --skill bet-on-itgit clone --depth 1 https://github.com/Lum1104/bet-on-itWrote 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/lum1104/bet-on-it/bet-on-it)<a href="https://agentmods.dev/skills/lum1104/bet-on-it/bet-on-it"><img src="https://agentmods.dev/badge/skills/lum1104/bet-on-it/bet-on-it/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/lum1104/bet-on-it/bet-on-it"><img src="https://agentmods.dev/badge/skills/lum1104/bet-on-it/bet-on-it.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 | $0.00023 | $0.00486 |
| Opus 5 | $0.00012 | $0.00243 |
| Sonnet 5 | $0.00005 | $0.00097 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
bet-on-it 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 12d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bet On It
Make each action earn or lose confidence. Never rewrite a prediction after its result.
Record the bet
When an action depends on an uncertain causal explanation, record this in working notes or commentary—not a repository file:
Hypothesis: <current causal explanation>
Expected observation: <specific result expected from the next action>
Expected change: <files, state, metric, or behavior; none for observation-only steps>
Disproof: <result that would make this hypothesis untenable>
Use observable signals. One bet may cover coupled steps. Skip bets for mechanical work or settled requirements.
Run the smallest discriminating step
Choose the cheapest safe step that separates plausible alternatives. Prefer read-only observation or temporary instrumentation before a persistent edit. Preserve baselines; separate unrelated changes.
Settle the bet
After the action, preserve the original prediction and append:
Observed: <actual result, including unexpected changes>
Verdict: matched | partially matched | failed | inconclusive
Next action: <continue, refine, abandon, revert, or gather a better signal>
Apply the verdicts consistently:
- Matched: Expected results occurred and disproof did not. This raises confidence but does not prove causality when rivals predict the same result.
- Partially matched: Some predictions held; narrow or revise the hypothesis.
- Failed: A valid discriminator produced disproof or failed a required prediction. Abandon or revise.
- Inconclusive: The step or measurement could not distinguish alternatives. Do not count it as support.
Revert speculative changes after a failed hypothesis when they lack independent justification. To keep one, state the new reason and test it under a new prediction.
Example
For a duplicate checkout request, Bet 1 predicts that instrumentation will show two client handler invocations; one invocation with two requests disproves it. Make no behavior change.
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.
- 12d ago First seen · 53 lines · 23 tokens per session scan A 196c67b1221d
bet-on-it is a skill published in the GitHub repository Lum1104/bet-on-it (5 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 486 once invoked, about $0.0001 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
triage
Investigate feedback, validate issues, and fix confirmed problems when requested.
spark-engineer
Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure…
debugging-wizard
Parses error messages, traces execution flow through stack traces, correlates log entries to identify failure points, and applies systematic hypothesis-driven methodology to isolate and resolve bugs. Use when investigating errors, analyzing stack traces, finding root causes of unexpected behavior, troubleshooting…
Reverse Engineering & Binary Analysis
Binary analysis, assembly interpretation, disassembly, decompilation, firmware RE, and protocol reverse engineering.
log-analyzer
A log-analysis helper that reads application and system logs to find unusual patterns and likely causes. Logs are records of events such as errors, requests, warnings, and service activity.
tidewave-integration
Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.