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 paruff/uFawkesAI --skill acceptance-criteriagit clone --depth 1 https://github.com/paruff/uFawkesAIWrote 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/paruff/ufawkesai/acceptance-criteria)<a href="https://agentmods.dev/skills/paruff/ufawkesai/acceptance-criteria"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/acceptance-criteria/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/paruff/ufawkesai/acceptance-criteria"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/acceptance-criteria.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 | $0.00025 | $0.00578 |
| Opus 5 | $0.00013 | $0.00289 |
| Sonnet 5 | $0.00005 | $0.00116 |
| Haiku 4.5 | $0.00003 | $0.00058 |
Grade A, and why
acceptance-criteria 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 8d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Acceptance Criteria Validation
Load trigger:
"load acceptance-criteria skill"> DORA: Cap 5 (Small Batches / Shift Left on Quality) Token cost: Low
Purpose
Validate that the build output satisfies all acceptance criteria.
Responsibilities
- Evaluate each acceptance criterion
- Mark pass/fail for each item
- Identify missing functionality
- Flag partially met criteria
Inputs
- Acceptance criteria (from
tasks.jsonor task files) - Build output (code, manifests, tests)
Outputs
acceptance-results.json
Validation Rules
Evaluation
- Each AC evaluated independently
- AC tested against actual implementation, not assumed
- Evidence cited for each pass/fail decision
- Partial implementations flagged (not marked as pass)
Completeness
- All ACs from all tasks accounted for
- No AC skipped without justification
- Cross-cutting ACs validated across all affected tasks
Ambiguity
- Ambiguous ACs flagged for human clarification
- ACs requiring runtime validation noted
- ACs dependent on external systems noted
AC Status Classification
| Status | Meaning |
|---|---|
| PASS | Implementation satisfies the AC completely |
| FAIL | Implementation does not satisfy the AC |
| PARTIAL | Implementation partially satisfies the AC |
| NOT TESTED | AC requires runtime testing not possible in review |
| AMBIGUOUS | AC is unclear and needs human clarification |
Output Format
{
"skill": "acceptance-criteria",
"status": "pass | fail",
"total": 12,
"passed": 10,
"failed": 1,
"partial": 1,
"results": [
{
"task_id": "TASK-001",
"ac_id": "AC-01",
"description": "Specific, testable assertion",
"status": "pass | fail | partial | not_tested | ambiguous",
"evidence": "File:Line or test reference",
"notes": "Any issues or ambiguities"
}
]
}
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.
- 8d ago First seen · 95 lines · 25 tokens per session scan A 52c8f06a59dc
acceptance-criteria is a skill published in the GitHub repository paruff/uFawkesAI (2 stars, last pushed 19d ago), licensed MIT. It adds 25 tokens to every session and 578 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 2026-09-03.
Other skills, from other repositories
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infrastructure-rules
Skill for the rules module — discovery, validation, scope, and private-sidecar symlink sync for the top-level rules/ directory (specifications include soft markdown guidelines and strong yaml/json formal constraints). Use when discovering rules (discoverrules), resolving a rule path (resolveruleroot), validating rule…
infrastructure-project
Skill for the project management infrastructure module providing multi-project discovery, structure validation, and metadata extraction. Use when discovering active projects, validating project directory structure, or extracting project configuration metadata.
template-formal
Strongly-typed multiagent ant-robot colony exemplar — ADTs, session-typed protocols, affine-discipline resource handles, storage-as-functor framing, Active-Inference-flavored decision loop, mypy-as-oracle negative controls.
template-reproducibility-audit
Deterministic reproducibility audit — fixed seeds, regenerate-from-clean, double-run diff before Zenodo/arXiv/release. USE WHEN outputs drift between runs, "worked on my machine", need regenerate-from-clean proof, or pre-release reproducibility check — even without naming docs/prompts.
template-test-creation
Create pytest suites under the no-mocks policy — real data, temp files, subprocess, pytest-httpserver. USE WHEN adding tests, raising coverage, testing new src/ module, or user forbids mocks.