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 truenorth-lj/adaptive-agent-skill --skill build-user-profilegit clone --depth 1 https://github.com/truenorth-lj/adaptive-agent-skillWrote 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/truenorth-lj/adaptive-agent-skill/build-user-profile)<a href="https://agentmods.dev/skills/truenorth-lj/adaptive-agent-skill/build-user-profile"><img src="https://agentmods.dev/badge/skills/truenorth-lj/adaptive-agent-skill/build-user-profile/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/truenorth-lj/adaptive-agent-skill/build-user-profile"><img src="https://agentmods.dev/badge/skills/truenorth-lj/adaptive-agent-skill/build-user-profile.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 | $0.00053 | $0.00825 |
| Opus 5 | $0.00026 | $0.00413 |
| Sonnet 5 | $0.00011 | $0.00165 |
| Haiku 4.5 | $0.00005 | $0.00082 |
Grade A, and why
build-user-profile 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 11d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build User Profile
Actively build and maintain a user profile in the agent's memory system.
When to Run
- First session in a new workspace — bootstrap the profile from observable context
- Profile feels stale — interests, focus areas, or tech stack have shifted
- User explicitly asks to update their profile
- After major project changes — new repo added, tech stack shift, role change
Step 1: Gather Observable Context
Collect signals from the workspace without asking the user. Run these in parallel:
1a. Git identity and activity
git log --all --format='%an <%ae>' | sort -u
git log --oneline -50 --all
git log --since='30 days ago' --oneline --all | head -20
1b. Project structure
ls -d */ 2>/dev/null
cat *.yaml *.yml 2>/dev/null | head -50
1c. Tech stack signals
Look for package.json, requirements.txt, go.mod, Cargo.toml, pyproject.toml, or similar. Read framework choices.
1d. Existing memory and feedback patterns
Check for memory files or CLAUDE.md that reveal preferences.
1e. Content signals
Check for blog posts, research docs, or README content that reveals interests.
Step 2: Read Existing Profile
Read the current user profile memory file if it exists. Note what's already captured and what might be outdated.
Step 3: Synthesize Profile
Build or update the profile with these sections:
---
name: {User} User Profile
description: {one-line} — used to tailor collaboration approach
type: user
---
## Role & Focus
- What they do, what projects they're working on
## Technical Strengths
- Languages, frameworks, tools they're strong in
- Areas they're learning or new to
## Work Style
- Autonomy level (do they want to be asked, or just do it?)
- Pace (ship fast vs deliberate?)
- Research habits (deep-dive vs pragmatic?)
## Communication Preferences
- Language preferences
- Verbosity (concise vs detailed?)
- Format preferences (tables, bullet points, prose?)
## Current Interests
- What they're researching or exploring right now
- Include date for staleness detection, e.g. (2026-04)
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.
- 11d ago First seen · 115 lines · 53 tokens per session scan A c4fa216c8696
build-user-profile is a skill published in the GitHub repository truenorth-lj/adaptive-agent-skill (2 stars, last pushed 4mo ago), licensed MIT. It adds 53 tokens to every session and 825 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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