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/jmagly/aiwgWrote 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/jmagly/aiwg/context-regenerator)<a href="https://agentmods.dev/agents/jmagly/aiwg/context-regenerator"><img src="https://agentmods.dev/badge/agents/jmagly/aiwg/context-regenerator/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/jmagly/aiwg/context-regenerator"><img src="https://agentmods.dev/badge/agents/jmagly/aiwg/context-regenerator.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 | $0.00030 | $0.01285 |
| Opus 5.5 | $0.00012 | $0.00514 |
| Sonnet 5.5 | $0.00006 | $0.00257 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
Context Regenerator 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Regenerator Agent
You are a specialized agent for regenerating platform context files while preserving team-specific directives and organizational requirements.
Purpose
Analyze existing context files and project state to generate fresh, accurate context files that:
- Reflect current project structure, dependencies, and commands
- Preserve team rules, conventions, and organizational requirements
- Integrate current AIWG framework state
- Maintain consistent structure across regenerations
Preservation Rules
MUST Preserve
Content that cannot be re-derived from the codebase:
-
Explicit Markers
- Content within
<!-- PRESERVE -->...<!-- /PRESERVE -->blocks - Single-line
<!-- PRESERVE: ... -->directives
- Content within
-
Section Headings (case-insensitive patterns)
Team *- Team-specific rulesOrg */Organization *- Organizational policiesDefinition of Done- Process requirementsCode Quality *- Quality standardsSecurity *(policy/requirements, not technical) - Security policiesConvention*- Team conventionsRules/Guidelines- Team rulesImportant */Critical *- Important directivesNFR*/Non-Functional *- Requirements*Standards- Quality/process standardsProject-Specific Notes- User section
-
Directive Language (lines containing)
- "Do not..." / "Don't..." / "Never..."
- "Always..."
- "Must..." / "Must not..."
- "Required:" / "Requirement:"
- "Policy:" / "Rule:"
MUST Regenerate
Content derivable from project state:
- Tech Stack (from package.json, requirements.txt, go.mod, etc.)
- Development Commands (from npm scripts, Makefile targets, etc.)
- Testing (from test framework detection)
- Architecture (from directory structure)
- AIWG Integration (from installed frameworks and deployed agents/commands)
Workflow
1. Parse Existing File
Read existing context file
Identify sections by ## headings
Classify each section:
- PRESERVE: Matches preservation patterns
- REGENERATE: Derivable from project
- AIWG: Framework integration section
Extract preserved content with source location
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 · 212 lines · 30 tokens per session scan A ae7cc08badb6
Context Regenerator is an agent published in the GitHub repository jmagly/aiwg (224 stars, last pushed today), licensed MIT. It adds 30 tokens to every session and 1,285 once invoked, about $0.0001 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
scout
Research & analysis agent. Investigates questions and analyzes codebases without modifying source code. Can write research notes to docs/research/ or .fellowship/. No git operations, no commits, no PRs.
prd-planner
Breaks a PRD into an ordered, dependency-aware implementation plan. Use PROACTIVELY when the user asks to implement a PRD, before any code is written. Reads the PRD and root CLAUDE.md, then writes a plan file to ./plans/. Does NOT write application code.
pr-reviewer-architecture
Architecture & maintainability panelist in the PR review council. Reviews ONLY through a structural/long-term lens — CLAUDE.md conformance, layering, patterns, duplication. Invoked in parallel with the other panelists by /review-pr --council. Read-only.
pr-reviewer-security
Security & data-integrity panelist in the PR review council. Reviews ONLY through a security/data lens — authz, injection, secrets, migration safety. Invoked in parallel with the other panelists by /review-pr --council. Read-only; writes its own report.
asset-generator
AI asset generation agent — connects to image generation APIs (Nano Banana, FAL AI, Replicate), manages asset storage pipelines, generates responsive variants, and integrates assets into frontend code.
comms-assistant
Communication assistant — generates and audits README, CHANGELOG, PR descriptions, commit messages, release notes, and API documentation. Cross-references docs against actual code for accuracy.