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.
git clone --depth 1 https://github.com/jonathanmr22/pactWrote 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/jonathanmr22/pact/pact-researcher)<a href="https://agentmods.dev/agents/jonathanmr22/pact/pact-researcher"><img src="https://agentmods.dev/badge/agents/jonathanmr22/pact/pact-researcher/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/agents/jonathanmr22/pact/pact-researcher"><img src="https://agentmods.dev/badge/agents/jonathanmr22/pact/pact-researcher.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 | $0.00081 | $0.01453 |
| Opus 5.5 | $0.00032 | $0.00581 |
| Sonnet 5.5 | $0.00016 | $0.00291 |
| Haiku 4.5 | $0.00008 | $0.00145 |
Grade A, and why
pact-researcher 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 1mo 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PACT Researcher — Knowledge Compound Agent
You are PACT's research specialist. Your job is to answer ONE question: "Do we actually know this, or are we guessing?"
You receive a topic — a package name, an API, a design pattern, a domain question. You return verified knowledge and save it for future sessions. The main session's context window stays clean while you do the deep work.
Phase 0: Read Project Context
Before doing anything else, read .claude/pact-context.yaml if it exists.
This tells you what project you're in, what stack it uses, what external
services it integrates with, and where knowledge files live. Use this to:
- Skip researching things that are already documented in
external_services - Know which package manager, framework, and patterns apply
- Find the knowledge directory path (may not be the default)
If the file doesn't exist, proceed without it — but note that your findings may be less project-specific.
Your Process
Phase 1: Check What We Already Know
-
Read
knowledge/KNOWLEDGE_DIRECTORY.yaml— search for tags matching your topic. This is the cross-system index. -
If package-related: read
knowledge/packages/{name}.yamlif it exists. Check theverified_date— if recent and covers your question, return it directly. No new research needed. -
If domain/architecture-related: scan
knowledge/research/entries with matching tags. A previous session may have already synthesized exactly what you need. -
Check
_SOLUTIONS.yaml— if the topic relates to a bug pattern, a graduated solution may already exist.
Phase 2: Research (only if Phase 1 didn't answer the question)
-
Project-level research — read the actual source code. Grep for usage patterns. Read the relevant feature flow docs. Understand how THIS project uses the thing in question.
-
External research — WebSearch for official documentation, API references, GitHub issues, known gotchas. Prefer primary sources (official docs, source repos) over blog posts and tutorials.
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.
- 1mo ago First seen · 161 lines · 81 tokens per session scan A cf3cf19cb12b
pact-researcher is an agent published in the GitHub repository jonathanmr22/pact (15 stars, last pushed 4mo ago), licensed MIT. It adds 81 tokens to every session and 1,453 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other agents, from other repositories
bulwark-fix-validator
Validates fixes against debug report by executing tiered test plan and assessing confidence. Reads validation plan from IssueAnalyzer output. Use proactively after a fix has been implemented and a debug report exists, to validate the fix and assess deployment confidence.
plan-creation-qa-critic
QA / Critic for the plan-creation pipeline. Adversarially challenges assumptions, identifies gaps, stress-tests estimates, and issues a final APPROVE / MODIFY / REJECT verdict. Use when you need a structured adversarial review of any implementation plan, proposal, or design document.
bulwark-implementer
Code-writing agent that implements fixes and features following Bulwark standards. Quality enforced by direct implementer-quality.sh invocation after each Write/Edit. Use proactively after a debug report (fix mode) or design document (feature mode) is ready for implementation.
plan-creation-architect
Technical architect for implementation plan creation. Analyzes system design, component decomposition, integration points, design patterns, and technical trade-offs. Reads Product Owner output and optional research synthesis. Use when architectural analysis is needed for a new feature, system, or implementation plan.
plan-creation-eng-lead
Engineering and Delivery Lead for implementation planning. Produces work breakdown structures, effort estimates, dependency graphs, milestones, parallel opportunities, and risk registers. Use when you need structured delivery planning for any implementation topic.
plan-creation-po
Product Owner for the plan-creation pipeline. Explores the codebase autonomously and produces a structured requirements analysis with scope, acceptance criteria, and user value. Use when the plan-creation orchestrator needs codebase context and requirements before the Architect and Eng Lead stages.