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 skills/idavidov13/agentic-playwright/ai-native-workflownpx skills add idavidov13/agentic-playwright --skill ai-native-workflowgit clone --depth 1 https://github.com/idavidov13/agentic-playwrightWrote 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/idavidov13/agentic-playwright/ai-native-workflow)<a href="https://agentmods.dev/skills/idavidov13/agentic-playwright/ai-native-workflow"><img src="https://agentmods.dev/badge/skills/idavidov13/agentic-playwright/ai-native-workflow.svg" alt="Measured on agentmods" 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 | $0.00188 | $0.02862 |
| Opus 5 | $0.00094 | $0.01431 |
| Sonnet 5 | $0.00038 | $0.00572 |
| Haiku 4.5 | $0.00019 | $0.00286 |
Grade A, and why
ai-native-workflow 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 4d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Native Workflow
Routing layer between user intent and the specialized skills that own the rules. Load first on every non-trivial task.
For deeper context: see references/three-layer-model.md, references/conversation-contract.md, references/principles.md, references/examples.md, references/troubleshooting.md.
Critical
- Low confidence means Phase 3 is incomplete. Would-be confidence < 5 → do NOT emit Phase 4; return to Phase 3 and ASK the user. Full thresholds live in one place — "Phase 4 — Confidence-Gate Format" below; other docs link there and never restate them. This rule is the most leveraged in the workflow: it stops plausible-looking plans built on guesses.
- Ask, don't invent. Never guess folder names, file paths, env-var names, enum values, credentials, or message strings.
ls,grep,playwright-cli, OpenAPI — or ask. - Refuse placeholders. Guessed selectors, unverified message strings, made-up enum values, secret-shaped strings — refuse and re-explore.
TODO/skeleton/ "to fill in later" outputs count as placeholders too — offering a "skeleton page object with TODO locators while we wait forplaywright-cli" is the same failure mode as inventing locators outright; don't. - Verify the user's premise even in Direct Mode. Before applying a one-line fix, confirm the reported defect actually exists (the typo on the cited line, the import the user wants removed, the value the user says is currently set). If the premise doesn't match the file, switch out of Direct Mode and ASK — applying a "fix" to a defect that isn't there invents a change.
- Specialized skills own the rules. This skill never restates rules from
api-testing,page-objects, etc. It tells you which skill to load and in what order. CLAUDE.mdConstitution is the safety floor. MUST/SHOULD/WON'T tables are hard stops; they take precedence over any prose, template, or example.- Audit-then-edit by default. For any non-trivial change, follow the 8-phase workflow below. Phase 4 (Plan + Confidence) is mandatory before Phase 6 (Apply).
- Confidence gate is required. Every Plan output must include a 1-10 confidence + rationale + unknowns block. See "Phase 4" below.
- Exploration is non-negotiable. UI →
playwright-clionly (no IDE browser MCP, no Cursor browser, noplaywright codegen). API → OpenAPI/docs first, live HTTP only as fallback. - One skill at a time. Load skills sequentially per the routing table. Don't stack 5 skills' Critical blocks before starting work.
- After any test edit, run the affected tests. On red, load
debugging— never suppress, never bump timeouts.
What ships with it
6 files 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.
- 4d ago First seen · 124 lines · 188 tokens per session scan A ea5fc5941267
ai-native-workflow is a skill published in the GitHub repository idavidov13/agentic-playwright (159 stars, last pushed today), licensed MIT. It adds 188 tokens to every session and 2,862 once invoked, about $0.0009 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 2026-08-30.
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