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 commands/uipath/skills/generate-taskgit 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 | $0.03450 |
| Opus 5 | $0.00000 | $0.01725 |
| Sonnet 5 | $0.00000 | $0.00690 |
| Haiku 4.5 | $0.00000 | $0.00345 |
Grade A, and why
generate-task 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 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.
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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Test Task
Generate one coder-eval task YAML (and an optional check script) for the scenario described in $ARGUMENTS.
Input: $ARGUMENTS — a free-form description of the scenario the task should cover. The target skill is always inferred from the description (Phase 1a). Do not require or accept a skill name as a separate argument.
Optional tier filter. If the description contains a bare smoke / integration / e2e token (whitespace-separated), use it as the tier hint. Otherwise pick the tier from the scenario itself per the Phase 2a table.
Output: ONE task YAML at tests/tasks/<skill-name>/... plus an optional check_*.py for e2e tasks. Always one task per invocation — re-run the command for additional scenarios.
⚠ Generated tasks are unverified scaffolds
The author MUST before merging:
- Run the task end-to-end with
coder-eval(command in the Phase 4 summary).- Confirm it passes.
- State that explicitly in the PR description (e.g.
Ran <task-id> locally and it passed.).The lint workflow at
.github/workflows/lint-tasks.ymlraises a High issue when the PR description doesn't claim a passing run. Pushing the YAML alone is not enough.
Phase 1 — Context Gathering
1a. Infer the target skill from the description
- List candidates via
ls skills/uipath-*/SKILL.md. - Read every
SKILL.mdfrontmatter (name + description) — that is what the runtime uses to decide which skill activates on a given prompt. - Match the input description to the skill whose frontmatter best covers it. Tiebreak by inspecting the candidates'
references/andassets/filenames. - If two or more skills are plausible, ask the user to disambiguate before proceeding. Do not guess.
Once resolved, <skill-name> is fixed for the rest of the run.
1b. Read context (parallel Explore agents or parallel tool calls)
skills/<skill-name>/SKILL.mdplus everything underskills/<skill-name>/references/andskills/<skill-name>/assets/.tests/README.md— authoritative source for the Tag Taxonomy, Weight scale, and experiment defaults (smoke.yamlfor PR-gate,default.yamlfor nightly e2e and ad-hoc,smoke-windows.yamlfor Windows RPA). Do not duplicate that material here; reference it..claude/commands/lint-task.md— the quality rubric (six axes, four severities). Generated tasks must not trip a Medium-or-above issue on any axis.tests/reports/<skill-name>.mdif it exists — use it to detect that the scenario in the description is already a known gap with prior recommendations.- Every existing
*.yamltask undertests/tasks/— collect alltask_idvalues (collision check), study conventions for the target skill (or a peer skill if the target has none yet), and confirm the described scenario is not already covered.
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 · 236 lines · 0 tokens per session scan A 354c2b179853
generate-task is a command 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 3,450 tokens. 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.