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 Mallikarjun-Roddannavar/playwright-agentic-automation --skill full-stack-incident-analystgit clone --depth 1 https://github.com/Mallikarjun-Roddannavar/playwright-agentic-automationWrote 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/mallikarjun-roddannavar/playwright-agentic-automation/full-stack-incident-analyst)<a href="https://agentmods.dev/skills/mallikarjun-roddannavar/playwright-agentic-automation/full-stack-incident-analyst"><img src="https://agentmods.dev/badge/skills/mallikarjun-roddannavar/playwright-agentic-automation/full-stack-incident-analyst/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/mallikarjun-roddannavar/playwright-agentic-automation/full-stack-incident-analyst"><img src="https://agentmods.dev/badge/skills/mallikarjun-roddannavar/playwright-agentic-automation/full-stack-incident-analyst.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 | $0.00097 | $0.01108 |
| Opus 5 | $0.00048 | $0.00554 |
| Sonnet 5 | $0.00019 | $0.00222 |
| Haiku 4.5 | $0.00010 | $0.00111 |
Grade A, and why
full-stack-incident-analyst 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 10d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Full-Stack Incident Analyst
Work from the repository root. Prefer repository and supplied runtime evidence over assumptions. Keep product-specific facts out of reusable guidance.
Mandatory preflight
- Read every applicable
AGENTS.mdand repository-local skill before editing. - Inventory the repository and named incident artifacts. Start from exact user-named paths.
- Run
python <skill>/scripts/ensure_source_index.py --repo . --quiet. - If the index is unavailable, do not query it. Continue with capped
smart_rg.pysearches and exactread_slice.pyverification.
Use the skill directory that contains this file for <skill>.
Select a mode
Begin every response with exactly one line:
Selected mode: Focused Evidence Answerfor a narrow lookup, one failing request, one UI state, one test failure, or an explicit no-RCA request.Selected mode: Strict Full Evidence RCAfor final root cause, classification, closure/support wording, complete validation, a fix recommendation, or implementation.
Focused mode must not claim final RCA, final classification, closure wording, or a definitive fix. Strict mode must list material evidence gaps and use Needs more evidence when a missing class could change the conclusion.
Route references
Load only the references needed for the request:
| Need | Load |
|---|---|
| Investigation, cross-layer tracing, implementation | references/incident-workflow.md |
| HAR, logs, screenshots, recordings, ticket text, DevTools | references/artifact-playbooks.md |
| Fix recommendation or code/config/test changes | references/fix-guidance.md |
| Focused answer, strict RCA, or completion report | references/output-contracts.md |
| Skill maintenance or behavior evaluation | references/evaluations.md |
Bounded discovery
Use this order and expand only when evidence remains insufficient:
- Query a known symbol, label, endpoint, test ID, error, or route:
python <skill>/scripts/query_source_index.py "<term>" --repo . --compact --limit 8. - Add
--edgesto trace imports, HTTP calls, routes, or test-ID definitions/usages. - Verify the best candidate immediately:
python <skill>/scripts/read_slice.py <file> --around "<anchor>" --context 12 --max-matches 2. - If absent or ambiguous, use:
python <skill>/scripts/smart_rg.py "<pattern>" . --max-files 6 --max-lines-per-file 3. - Use raw
rgonly after the index and capped search are insufficient.
What ships with it
11 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.
- agents/openai.yaml 269 B
- references/artifact-playbooks.md 3.2 KB
- references/evaluations.md 4.4 KB
- references/fix-guidance.md 3.6 KB
- references/incident-workflow.md 5.2 KB
- references/output-contracts.md 1.9 KB
- scripts/build_source_index.py 7.3 KB runs code
- scripts/ensure_source_index.py 4.9 KB runs code
- scripts/query_source_index.py 3.6 KB runs code
- scripts/read_slice.py 3.4 KB runs code
- scripts/smart_rg.py 3.3 KB runs code
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.
- 10d ago First seen · 84 lines · 97 tokens per session scan A c26d8122ccb5
full-stack-incident-analyst is a skill published in the GitHub repository Mallikarjun-Roddannavar/playwright-agentic-automation (11 stars, last pushed 4d ago), licensed MIT. It adds 97 tokens to every session and 1,108 once invoked, about $0.0005 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 2026-08-31.
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