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 harrystamatoukos/ai-conversation-extractor --skill who-am-igit clone --depth 1 https://github.com/harrystamatoukos/ai-conversation-extractorWrote 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/harrystamatoukos/ai-conversation-extractor/who-am-i)<a href="https://agentmods.dev/skills/harrystamatoukos/ai-conversation-extractor/who-am-i"><img src="https://agentmods.dev/badge/skills/harrystamatoukos/ai-conversation-extractor/who-am-i/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/harrystamatoukos/ai-conversation-extractor/who-am-i"><img src="https://agentmods.dev/badge/skills/harrystamatoukos/ai-conversation-extractor/who-am-i.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 | $0.00050 | $0.01615 |
| Opus 5 | $0.00025 | $0.00807 |
| Sonnet 5 | $0.00010 | $0.00323 |
| Haiku 4.5 | $0.00005 | $0.00161 |
Grade A, and why
who-am-i 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 12d 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Who Am I Really — Mirror Analysis Skill
Build a living portrait of the user from their conversation patterns. Not what they claim to be, but what their conversations reveal.
What to Analyze
Conversations reveal personality through these signals:
| Signal | What It Reveals |
|---|---|
| What they ask about | Curiosity, obsessions, anxieties |
| How they frame questions | Thinking style, mental models |
| What they push back on | Values, non-negotiables |
| What they return to repeatedly | Core concerns, unresolved tensions |
| What they avoid or never discuss | Fears, blind spots |
| How they talk about others | Social orientation, empathy style |
| How they make decisions | Risk tolerance, values hierarchy |
| Their emotional language | What matters most, stress patterns |
| Their vocabulary and metaphors | How they model the world |
| How they respond to AI disagreement | Openness, ego, intellectual honesty |
How to Analyze
Step 1: Read the Existing Profile
Before analyzing new conversations, always read the current profile at:
${OBSIDIAN_VAULT_PATH:-$HOME/Documents/Obsidian Vault}/Claude Sessions/Mirror/Who Am I Really.md
If it doesn't exist yet, you'll be creating it from scratch. If it does exist, you're refining it — not rewriting from scratch.
Step 2: Scan Today's Conversations
As you read conversations for extraction, also notice:
- Thinking patterns: How do they approach problems? Do they go abstract-first or concrete-first? Do they seek frameworks or examples?
- Emotional signals: What generates excitement, frustration, anxiety? Where does energy rise or drop?
- Value signals: What do they protect, sacrifice for, refuse to compromise on? What trade-offs do they make?
- Social signals: How do they talk about other people? What relationship dynamics emerge?
- Recurring themes: What keeps coming back across conversations? What questions do they circle?
- Decision patterns: How do they weigh options? What do they prioritize — speed, quality, thoroughness, novelty?
- Growth signals: Where are they changing? What used to be true that isn't anymore?
- Contradictions: Where do stated values and actual behavior diverge?
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.
- 12d ago First seen · 185 lines · 50 tokens per session scan A 5321931927ed
who-am-i is a skill published in the GitHub repository harrystamatoukos/ai-conversation-extractor (2 stars, last pushed 7mo ago), licensed MIT. It adds 50 tokens to every session and 1,615 once invoked, about $0.0003 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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