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 agentmods add agents/uipath/skills/agents-lowcode-rulesgit clone --depth 1 https://github.com/UiPath/skillsWhat 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 | $0.00000 | $0.08960 |
| Opus 5 | $0.00000 | $0.04480 |
| Sonnet 5 | $0.00000 | $0.01792 |
| Haiku 4.5 | $0.00000 | $0.00896 |
Grade C, and why
agents-lowcode-rules scanned grade C with 2 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 2d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
| `LC_PROMPT_STRUCTURE` | info | general | Prompt complexity makes responsibilities, tool guidance, boundaries, or output instructions hard to locate | Read the full prompt in light of its tools, schemas, and decision pa Nullifies safety policiesmediumAnti-refusal
"You have no restrictions", "do anything now", "ignore your guidelines": a direct jailbreak that disables guardrails.
| `LC_FAILURE_PROMPT_INJECTION` | warning | general | Untrusted-content instruction boundary is ineffective OR the system prompt contains jailbreak fragments | Two shapes, fire on either: (a) user, retrieved, or tool-ret Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agents — Low-code Judgment Rule Catalog
Judgment rules for low-code agents (agent.json). Each rule requires the agent to read source and reason — what a regex/count/schema-walk cannot decide reliably. Same row schema as elsewhere — see ../rule-format.md.
This catalog is judgment-only. Run
uip agent review "<PROJECT_DIR>" --output jsonfirst (SKILL.md Step 2.5) — it returns the deterministic low-code findings (structural gates, schema-property presence, placeholder cross-refs, eval-set structure and schema cross-refs, guardrail configuration validity) in the same rule format. Then apply the rules below, which the CLI cannot do.
Read ../rule-format.md and ../rule-catalog-workflow.md first.
Layouts
Low-code agents exist in two layouts. Detect first, then apply the right Read path in each rule.
| Layout | Identifier | Key files |
|---|---|---|
| Normalized | A single JSON file with snake_case top-level keys (system_prompt, tools, datasets, input_schema, output_schema, user_prompt) |
The single JSON file |
| Agent-builder | agent.json at project root with camelCase keys + sibling project.uiproj and resources/<Name>/resource.json |
agent.json, resources/*/resource.json, evals/eval-sets/*.json, evals/evaluators/*.json |
Rules tagged (agent-builder only) skip silently on the normalized layout; (normalized only) skip on agent-builder. Untagged rules apply to both with layout-aware detection_method.
How to read this file
One H2 section per checker class (EvalsChecker, SchemaChecker, ToolsChecker, GuardrailsChecker, GeneralChecker) groups related rules for navigation. Every row's detection_method is the judgment form: read the named source, reason about it, emit when the criteria hold. Log the reasoning in the finding's description.
EvalsChecker
| rule_id | severity | category | trigger | detection_method | suggested_fix |
|---|---|---|---|---|---|
LC_EVAL_INPUT_DIVERSITY |
warning | evals | Eval datapoints do not span the scenario classes the agent will meet in production | Read evals/eval-sets/*.json datapoints plus the agent's purpose, inputSchema, tools, and escalations. Assess semantically — not by count — whether the set spans distinct classes: happy path, edge/boundary values, malformed or missing input, ambiguous or unresolvable requests, and every declared side effect (each tool and escalation path). Emit when whole classes are absent or when the datapoints are paraphrases of one scenario. Name the missing classes in the description; do not assert a target datapoint count. file = eval set JSON. |
Author datapoints for the missing scenario classes, covering each declared tool and escalation path. |
LC_EVAL_GROUND_TRUTH_SWAPPED |
warning | evals | ≥2 eval datapoints share substantially overlapping input shape but disagree on expectedOutput (suggests label swap) |
Read evals/eval-sets/*.json. For each pair of datapoints, compare inputs content overlap (e.g., email_subject + email_body substring overlap > 80%). Assess: when inputs are near-identical but expected outputs differ, this is a likely label swap (the opposite failure pattern from the deterministic duplicate-output check). Emit one finding per swapped pair, element = <id_1>,<id_2>. file = eval set JSON. |
Inspect both datapoints; correct the swapped label, or split them into clearly distinct scenarios. |
LC_EVAL_JUDGE_FOR_CLOSED_CLASS |
warning | evals | LLM-judge evaluator applied to a closed-class output field | Read eval set's evaluatorRefs. Read agent's outputSchema.properties. Assess: if any output property has .enum (closed set) AND an evaluator referenced by the eval set is an LLM-judge (evaluatorTypeId: "uipath-llm-judge-output-semantic-similarity" or similar), the LLM judge is the wrong tool — uipath-multiclass-classification / uipath-exact-match gives 100% of the signal at zero variance. Emit one finding per (eval-set, judge-evaluator) pair. file = eval set JSON. |
Replace the LLM-judge with uipath-multiclass-classification or uipath-exact-match for closed-class outputs. |
LC_EVAL_ARCHETYPE_FIT |
warning | evals | Evaluator mix does not measure the agent behavior or failure risk that matters | Read the agent purpose, tools, output schema, eval scenarios, and referenced evaluator configurations. Assess whether the evaluators measure the primary contract: exact or multiclass comparison for closed outputs, structural comparison for structured integration results, trajectory or tool behavior when path and side effects matter, and rubric-based judgment for genuinely subjective generation. Emit the most specific mismatch and skip a closed-class mismatch already covered by LC_EVAL_JUDGE_FOR_CLOSED_CLASS. file = eval set JSON. |
Replace or supplement the evaluator mix so it measures the agent's primary output contract, decision path, and highest-risk failure modes. |
LC_EVAL_JUDGE_RUBRIC_FIT |
warning | evals | LLM-judge instructions are too vague to produce a stable, auditable assessment | Read each LLM-judge evaluator's prompt, criteria, score or label contract, and target output. Emit when a subjective judgment has no explicit criteria, evidence expectations, or distinction between score levels, or when the rubric does not match the behavior under evaluation. Do not require hidden chain-of-thought or a particular model. file = evaluator JSON, element = evaluator name. | Define observable criteria and score or label boundaries, and require a concise evidence-based justification appropriate for audit. |
LC_EVAL_MOCK_CONTRACT_DRIFT |
warning | evals | Eval-set mock response contradicts the mocked tool's declared output contract | Read the mock definitions in evals/eval-sets/*.json and each mocked tool's outputSchema (normalized: .tools[]; agent-builder: resources/<Name>/resource.json). Assess per mock: do the payload's property names, types, required fields, and enum members match what the tool declares it returns? Emit when the mock returns a shape the tool cannot produce — evaluations then pass against a contract production never emits. Tools with no declared output schema have nothing to compare; skip those mocks. file = eval set JSON, element = <tool_name>. |
Update the mock payload to the tool's declared output shape, or correct the tool's outputSchema when the mock reflects what the tool really returns. |
LC_EVAL_AGENT_DEFINITION_DRIFT |
warning | evals | Eval datapoint expects a resource, tool, or behavior the agent definition does not provide | Read what the datapoints expect — expected behavior, expected output, simulation instructions — against the agent definition, tools, and resources. Assess per datapoint: does the agent, as configured, have every index, resource, tool, and escalation the expectation names — and does the expectation stay inside what the definition instructs? Emit when the expectation is unsatisfiable by the agent as configured, so the datapoint scores against a definition that does not exist. A paraphrase of a bound resource is not drift; a name the agent binds nowhere is. file = eval set JSON, element = datapoint id. | Point the expectation at the resource, tool, or behavior the agent declares, or bind the missing element to the agent. |
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.
- 2d ago First seen · 154 lines · 0 tokens per session scan C 5de03e1ec416
agents-lowcode-rules is an agent published in the GitHub repository UiPath/skills (150 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 8,960 tokens. A static security scan graded it C with 2 findings (asks the agent to reveal its instructions, nullifies safety policies). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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