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 tomzx/agents --skill review-assumption-validationgit clone --depth 1 https://github.com/tomzx/agentsWrote 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/tomzx/agents/review-assumption-validation)<a href="https://agentmods.dev/skills/tomzx/agents/review-assumption-validation"><img src="https://agentmods.dev/badge/skills/tomzx/agents/review-assumption-validation/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/tomzx/agents/review-assumption-validation"><img src="https://agentmods.dev/badge/skills/tomzx/agents/review-assumption-validation.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 | $0.00030 | $0.01293 |
| Opus 5 | $0.00015 | $0.00647 |
| Sonnet 5 | $0.00006 | $0.00259 |
| Haiku 4.5 | $0.00003 | $0.00129 |
Grade A, and why
review-assumption-validation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Assumption Validation
Audits an assumption validation report and reports findings across five categories: completeness, experiment quality, result rigor, impact assessment, and verdict soundness.
Prerequisites
- Apply the shared SDLC conventions in
skills/sdlc/references/shared.md. - If no argument is provided, locate the feature directory under
.sdlc/features/whose frontmatterissuefield references$ISSUE_NUMBER. .sdlc/features/N-<slug>/assumption-validation.md, or a validation report provided in context or as a file path- Access to
.sdlc/knowledge/assumptions/to cross-check assumption statuses
Steps
- Read the assumption validation report from
.sdlc/features/N-<slug>/assumption-validation.mdif present, otherwise from context or as a file path. - Read the assumption records in
.sdlc/knowledge/assumptions/to cross-check that statuses were updated consistently with the report's claims. - Read the design artifacts (
specification.md,plan.md,tasks/) to verify that invalidated assumptions were followed by artifact revisions where needed. - Evaluate the report against the checklist below.
- Report findings by category. Omit categories with no findings.
- Write the findings to
.sdlc/features/N-<slug>/review-assumption-validation.mdwith frontmatterartifact: assumption-validation,verdict(approved/changes-requested/rejected), andreviewed_at: <ISO date>, and the findings as the body, perskills/sdlc/references/shared.md.
Review Checklist
Completeness
- Were all Active assumptions with High or Medium risk considered for validation?
- Were implicit assumptions in the design artifacts scanned for, not just formally recorded ones?
- Were Low-risk assumptions listed for reference even if not validated?
- If the report claims no assumptions needed validation, is that claim justified by the artifacts?
Experiment Quality
- Was each experiment the cheapest decisive test available for the assumption?
- Was a pass/fail threshold set before running each experiment, not after?
- Did each experiment actually test the stated assumption, or did it test something adjacent?
- Were spike experiments kept minimal and disposable, not creeping toward implementation?
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.
- 6d ago First seen · 118 lines · 30 tokens per session scan A 4d40db2ab002
review-assumption-validation is a skill published in the GitHub repository tomzx/agents (6 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 1,293 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.