Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/duc01226/EasyPlatformnpx agentmods add skills/duc01226/easyplatform/experience-reviewWrote 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/duc01226/easyplatform/experience-review)<a href="https://agentmods.dev/skills/duc01226/easyplatform/experience-review"><img src="https://agentmods.dev/badge/skills/duc01226/easyplatform/experience-review/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/duc01226/easyplatform/experience-review"><img src="https://agentmods.dev/badge/skills/duc01226/easyplatform/experience-review.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 | $0.00046 | $0.03879 |
| Opus 5.5 | $0.00018 | $0.01552 |
| Sonnet 5.5 | $0.00009 | $0.00776 |
| Haiku 4.5 | $0.00005 | $0.00388 |
Grade A, and why
experience-review 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 3d 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex compatibility note:
- Invoke
$skill-name: load the registered.agents/skills/<name>/SKILL.mdand execute it with Codex tools; no separateSkilltool or Claude mode switch is needed. Author.claude/**; mirrors are generated.- Track steps before execution and keep progress/evidence current. Explicit skill requests execute their protocol. Use the native question tool for required questions.
- Map foreign tool names to available host capabilities. A name mismatch needs no approval; an actually unavailable required capability stops with its step/evidence and a user question before adaptation.
- Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required
spawn_agentsubagent(s) for that task.- Preserve protocol order unless the user approves a deviation. Workflows use
$start-workflow: gates stay fixed; other steps may flex with logged reasons and evidence.
Quick Summary
Review modes: For review/audit work, standalone defaults to
--review-only(--report-onlyalias);--fix-loopenables review → validate → authorized fix → fresh re-review, default cap 3.--fix-loop --loop-owner=callerreturns a read-only pass to the caller that owns fixes/re-review. Create triage, review, validation and re-review todo tasks first; apply the carriedreview-policycontract for LOW deferral and user-approved bounded extensions. Non-review modes and terminal findings validation keep their own dispatch.
Goal: Exercise the real interface and inspect evidence so acceptance decisions reflect observed behavior.
Summary: Resolve intent → plan evidence/capability → start, instrument and exercise → inspect/judge → repair/re-exercise within budget → compare, tear down and hand off. Keep observations, judgments and human acceptance separate.
Key Rules:
--rounds=N: integers0–3; review-only defaults to0, standalone--fix-loopdefaults to3. A numeric budget never grants repair authority; caller-owned passes stay read-only. State mode and budget before observation.- Keep the evidence matrix and expectations intact; only BLOCKING defects open repair rounds.
- A clean review recommends acceptance; explicit owner acceptance is required before promoting any baseline.
Workflow: Follow the six steps in order; track each with evidence or an explicit skip reason using host task tools or an equivalent tracker. Source reading, test-writing and artifact generation alone never verify an experience.
What ships with it
1 file 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.
- 3d ago First seen · 183 lines · 46 tokens per session scan A 385325fa5a32
experience-review is a skill published in the GitHub repository duc01226/EasyPlatform (12 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 3,879 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 2026-10-18.
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