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 arc-mcp/arc-1 --skill analyze-chat-sessiongit clone --depth 1 https://github.com/arc-mcp/arc-1Wrote 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/arc-mcp/arc-1/analyze-chat-session)<a href="https://agentmods.dev/skills/arc-mcp/arc-1/analyze-chat-session"><img src="https://agentmods.dev/badge/skills/arc-mcp/arc-1/analyze-chat-session.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 53 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00074 | $0.03527 |
| Opus 5 | $0.00037 | $0.01764 |
| Sonnet 5 | $0.00015 | $0.00705 |
| Haiku 4.5 | $0.00007 | $0.00353 |
Grade A, and why
analyze-chat-session 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 8d 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 — 337 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Chat Session
Analyze the current conversation's tool calls, responses, errors, and overall approach to produce a structured feedback report. The goal is continuous improvement of MCP tool usage patterns, prompt strategies, and ARC-1 server capabilities.
This skill is introspective — the LLM analyzes its own chat session (the conversation you're currently in, or a referenced one) and generates actionable feedback.
PRIVACY NOTICE — READ FIRST
Before using this skill's output outside the current system (e.g., pasting into a GitHub issue, sharing in a chat), the user MUST review it for sensitive data.
The analysis MUST NOT contain:
- SAP system URLs, hostnames, IP addresses, or ports
- Usernames, passwords, API keys, tokens, or credentials
- Client numbers, system IDs (SID), or environment identifiers
- Real business data (customer names, order numbers, financial values, employee data)
- File paths that reveal internal infrastructure (home directories, mount paths)
- Transport request numbers or package names that reveal internal naming conventions
- Any content from SAP table previews or SQL query results containing business data
The LLM must actively redact these from the output, replacing them with generic placeholders like <SAP_HOST>, <USERNAME>, <OBJECT_NAME>, <TABLE_DATA_REDACTED>, etc.
After generating the report, remind the user: "Please review this report for any remaining sensitive information before sharing it outside your organization."
Smart Defaults (apply silently, do NOT ask)
| Setting | Default | Rationale |
|---|---|---|
| Output format | issue (GitHub issue-ready) |
Most actionable format |
| Severity filter | All levels | Don't miss anything |
| Focus area | Entire session | Comprehensive analysis |
| Sensitive data | Redact all (see Privacy Notice) | Safe by default |
Proceed immediately with Smart Defaults when triggered. No questions needed.
Input
The user triggers this skill at any point during (or after) a conversation. No additional input is required — the LLM analyzes the current chat context. Proceed immediately with Smart Defaults.
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.
- 8d ago First seen · 337 lines · 74 tokens per session scan A c1facbe7d72c
analyze-chat-session is a skill published in the GitHub repository arc-mcp/arc-1 (182 stars, last pushed today), licensed MIT. It adds 74 tokens to every session and 3,527 once invoked, about $0.0004 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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