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/keychain-io/trustable-ai/claudegit clone --depth 1 https://github.com/keychain-io/trustable-aiWrote 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/agents/keychain-io/trustable-ai/claude)<a href="https://agentmods.dev/agents/keychain-io/trustable-ai/claude"><img src="https://agentmods.dev/badge/agents/keychain-io/trustable-ai/claude.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.02113 |
| Opus 5 | $0.00000 | $0.01056 |
| Sonnet 5 | $0.00000 | $0.00423 |
| Haiku 4.5 | $0.00000 | $0.00211 |
Grade A, and why
CLAUDE 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 4d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agents
Purpose
Solves context overload (#3) and implements Agent Specialization (Pillar #3) from VISION.md.
When AI operates in overloaded context windows with mixed responsibilities:
- Instructions from early conversation get forgotten
- Constraints ignored due to context pollution
- Work skipped because agent lost track of requirements
- Hallucinations increase as context window fills
The agent system implements fresh context per role - each agent spawns in a clean context window with only the information needed for its specific responsibility. No conversation history, no accumulated cruft, just focused execution.
Key Agents
business-analyst
Problem Solved: Business value and priority decisions made without structured analysis
Analyzes backlog items for business value, revenue impact, customer benefit, and strategic alignment. Returns prioritized recommendations.
Real Failure Prevented: Feature prioritization happens in conversation. "Let's do the shiny feature first!" Ship feature with low ROI, delay high-value work. With business analyst: data-driven prioritization shows auth feature has 10x revenue impact vs UI polish. Build auth first.
project-architect
Problem Solved: Technical decisions made without architecture review or risk analysis
Reviews proposed features for technical feasibility, architecture patterns, integration complexity, and risks.
Real Failure Prevented: Engineer implements real-time updates with WebSockets. Architect review reveals: existing infra can't handle WebSocket connections, requires $5k/month infrastructure upgrade. Alternative: SSE with existing infra, zero extra cost.
senior-engineer
Problem Solved: Story point estimates made without task breakdown or historical data
Breaks features into granular tasks, estimates effort based on complexity, identifies unknowns.
Real Failure Prevented: Feature estimated at "3 points". Implementation reveals 8 integration points, 12 edge cases, security review needed. Actual: 13 points. With senior engineer breakdown: accurate 13-point estimate upfront, sprint capacity planned correctly.
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.
- 4d ago First seen · 213 lines · 0 tokens per session scan A bac8838baf40
CLAUDE is an agent published in the GitHub repository keychain-io/trustable-ai (2 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,113 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 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.