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 a1024053774/design-integrity-guardrails --skill behavioral-acceptance-reviewgit clone --depth 1 https://github.com/a1024053774/design-integrity-guardrailsWrote 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/a1024053774/design-integrity-guardrails/behavioral-acceptance-review)<a href="https://agentmods.dev/skills/a1024053774/design-integrity-guardrails/behavioral-acceptance-review"><img src="https://agentmods.dev/badge/skills/a1024053774/design-integrity-guardrails/behavioral-acceptance-review/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/a1024053774/design-integrity-guardrails/behavioral-acceptance-review"><img src="https://agentmods.dev/badge/skills/a1024053774/design-integrity-guardrails/behavioral-acceptance-review.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 | $0.00062 | $0.00862 |
| Opus 5.5 | $0.00025 | $0.00345 |
| Sonnet 5.5 | $0.00012 | $0.00172 |
| Haiku 4.5 | $0.00006 | $0.00086 |
Grade A, and why
behavioral-acceptance-review 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 27d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Behavioral Acceptance Review
Act as an acceptance auditor, not the implementation agent, test author, or general
code reviewer. Review one frozen candidate snapshot in a fresh, read-only context. Do
not read the implementation agent's chat, rationale, or hidden planning notes. The caller
passes the repository path, the candidate, and the acceptance claim; without them, return
INCOMPLETE. Do not
edit files, tests, fixtures, branches, commits, deployments, or external systems.
When the claimed behavior depends on a domain rule, a real external boundary, timing,
data semantics, permissions, deployment, or another condition that a mock cannot establish,
require evidence from that boundary (or clearly mark the result INCOMPLETE). A host mock
or simulator may prove pure logic, but it is not independent evidence of the boundary it
replaces. Coordinate with the reality-first-engineering gate; do not recreate its ledger
or turn this review into a second architecture exercise.
Agent products
When the target is a model-driven Agent product, also read references/agent-products.md: the capability profile, evidence layers, weak assertions, severity, release verdict, and rules for automated judges.
Establish independence
Rebuild the acceptance criteria from the user's task, public contract, and inputs external to the implementation. Tests added or modified in the current change are untrusted evidence, not the oracle. Inspect them for leakage, but do not accept a green suite as the only proof.
Check that:
- The test observes the real public or user-visible entry point.
- Inputs are supplied outside production code and are not copied into it.
- Expected values and oracles do not import or duplicate production constants, helpers, templates, or generated answers.
- At least one unseen, metamorphic, or sentinel input is exercised.
- A known-bad implementation would fail: hard-coded output, ignored input, always-success/empty output, or a test-only branch.
- Assertions prove the claimed behavior rather than replaying one fixed example or weakening the requirement.
- For generated text or artifacts, structure, provenance, input dependence, and required fields are checked; exact prose snapshots alone are insufficient.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 27d ago Changed · +5 lines b5ee8a4dd58b
- 1mo ago Changed · +8 lines · +12 tokens per session 10bf5c7a408e
- 2mo ago First seen · 70 lines · 50 tokens per session scan A 8322652004fe
behavioral-acceptance-review is a skill published in the GitHub repository a1024053774/design-integrity-guardrails (0 stars, last pushed 28d ago), licensed MIT. It adds 62 tokens to every session and 862 once invoked, about $0.0002 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
gentle-ai-bench
Trigger: bench, journey, journeys, driven mode, gentle-ai-bench, journey corpus, j-numbers, bench axis. Author and verify gentle-ai bench journeys; go test ./bench never proves driven execution.
go-testing
Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.
include-test-files-that-assert-on-behavior-being-changed-in-decl
When delegating a task affected by this skill, include.
Autonomous Agent Testing
Testing patterns for autonomous AI coding agents like Devin and SWE-Agent including task verification, output validation, sandboxed execution, regression testing for agent behavior, and safety guardrails for autonomous code generation.
ring:running-dev-cycle
Running the backend dev cycle: implements every task in a rolling-wave plan.md (ring:writing-plans format) for a Go/TS service, driving specialist agents through Gate 0 implementation/TDD, Gate 8 parallel review, and Gate 9 validation per epic, elaborating later phases at each phase boundary. Use when starting or…
ring:instrumenting-streaming-events
Instrumenting streaming events: wires lib-streaming event emission end-to-end into a Lerian Go service via a 13-gate cycle (catalog, Builder bootstrap, Emit sites, outbox, HTTP manifest, NoopEmitter fallback, integration and chaos tests), dispatching ring:backend-go under TDD. Consumes the validated…