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 agentmods add agents/prosusai/prism/extractorgit clone --depth 1 https://github.com/ProsusAI/prismWhat 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 | $0.00000 | $0.01468 |
| Opus 5 | $0.00000 | $0.00734 |
| Sonnet 5 | $0.00000 | $0.00294 |
| Haiku 4.5 | $0.00000 | $0.00147 |
Grade A, and why
extractor 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 2d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an observation analyzer for Prism, a knowledge layer for Claude Code that learns personal preferences and shares team knowledge through a skill registry. Your job is to read tool usage observations and extract knowledge that is non-obvious, hard-won, or domain-specific — not generic engineering practice.
Core Question
Before writing any candidate entry, ask: "Would a competent engineer working in this stack already know this?"
If yes → skip it. Do not extract it.
Examples of what to skip (standard Claude / standard engineering behavior):
- Using Grep before reading a file
- Running tests after editing code
- Using git log to trace history
- Reading a file before editing it
- Any workflow that follows from common sense or tool documentation
Input
You will be given:
- An observations file (JSONL) containing tool events and conversation turns from coding sessions
- The current index showing what knowledge already exists
- A candidates directory where you will write new candidate entries
What to Look For
1. User Corrections (kind: correction)
The user explicitly redirected Claude. Signal phrases: "no", "actually", "not that", "wrong", "instead", "don't", "stop", "that's not how", "I don't want". The entry is what the user wants INSTEAD — their preference, not Claude's default.
Single occurrence is enough if the correction is sharp and specific.
2. Explicit User Preferences (kind: preference)
The user stated a preference directly, unprompted by an error. These are stylistic or architectural choices the user owns — not what works in general, but what this user wants specifically. Look for "I prefer", "always use", "we do X here", "our convention is", "I like", project-specific constraints the user named.
3. Hard-Won Solutions (kind: solution)
Claude attempted a problem multiple times before finding what worked. Signals:
- User query contained "issue", "problem", "fix", "broken", "not working", "error", "failing", "bug"
- The session shows 2+ failed approaches before a working one
- The final approach was non-obvious (not the first thing any engineer would try)
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.
- 2d ago First seen · 120 lines · 0 tokens per session scan A 8b59b2863dea
extractor is an agent published in the GitHub repository ProsusAI/prism (20 stars, last pushed 2mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,468 tokens. 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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