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 manishiitg/coding-agent-loop --skill authentication-session-validationgit clone --depth 1 https://github.com/manishiitg/coding-agent-loopWrote 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/manishiitg/coding-agent-loop/authentication-session-validation)<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/authentication-session-validation"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/authentication-session-validation/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/manishiitg/coding-agent-loop/authentication-session-validation"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/authentication-session-validation.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 | $0.00039 | $0.00643 |
| Opus 5.5 | $0.00016 | $0.00257 |
| Sonnet 5.5 | $0.00008 | $0.00129 |
| Haiku 4.5 | $0.00004 | $0.00064 |
Grade A, and why
authentication-session-validation 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 1mo 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Authentication and Session Validation
Outcome
Create repeatable, role-aware tests for the application's approved authentication and session lifecycle with durable results and attempt-scoped evidence.
When to use
Use after Basic Browser Setup when authentication behavior needs coverage beyond establishing one working test session. Include only flows enabled and authorized for the customer environment.
Discovery and user direction
Inspect the current workflow, goals, metrics, configuration, capabilities, stores, reports, and triggers before proposing changes. Summarize reusable design and gaps, then ask focused questions for unresolved customer choices such as scope, success, approvals, thresholds, ownership, and budgets. Record the answers as customer direction. Installation alone does not approve workflow changes or execution. Default to one small-team workflow; split only for incompatible access or lifecycle boundaries.
Required inputs
Resolve authentication methods, actor roles, approved expectations, account ownership, MFA/recovery channels, session lifetime rules, lockout limits, allowed state changes, evidence retention, and cleanup/reset procedures.
Plan and AgentWorks tools
Use scripted steps for repeatable Playwright scenarios and deterministic persistence. Use a message sequence for evidence-based investigation. Add branches only for real outcome paths or configured review. Reuse selected secrets and never pass secret values through plan context.
Knowledge and persistence
Keep secret references in workflow configuration. Store verified routes, session behavior, role requirements, and non-secret auth quirks in application KB notes. Store runs, cases, attempts, and artifact metadata in the DB and video plus redacted console/network evidence in durable assets.
Validation and reporting
Require a terminal result for every expected auth scenario and actor. Verify fresh-context behavior, post-action state, required cleanup, source/build identity, and evidence status. The dashboard shows the scenario/actor matrix, environment and build, failure class, evidence status, and history without exposing credentials or tokens.
What ships with it
3 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.
- 1mo ago First seen · 52 lines · 39 tokens per session scan A 723fd2c791de
authentication-session-validation is a skill published in the GitHub repository manishiitg/coding-agent-loop (13 stars, last pushed 19d ago), licensed MIT. It adds 39 tokens to every session and 643 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-14.
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