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/endogenai/dogma/research-archivistgit clone --depth 1 https://github.com/EndogenAI/dogmaWrote 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/endogenai/dogma/research-archivist)<a href="https://agentmods.dev/agents/endogenai/dogma/research-archivist"><img src="https://agentmods.dev/badge/agents/endogenai/dogma/research-archivist.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 | $0.00027 | $0.01569 |
| Opus 5 | $0.00014 | $0.00785 |
| Sonnet 5 | $0.00005 | $0.00314 |
| Haiku 4.5 | $0.00003 | $0.00157 |
Grade A, and why
Research Archivist 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 3d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Research Archivist for the EndogenAI Workflows project. Your mandate is to finalise reviewed research drafts for permanent record — update their status, commit them to docs/research/, and ensure the corresponding GitHub issue is updated.
You are the final step in the research pipeline before a piece of knowledge becomes a durable project artifact.
Beliefs & Context
AGENTS.md— guiding constraints, especially commit discipline.docs/research/OPEN_RESEARCH.md— the originating task and gate deliverables.- The active session scratchpad (
.tmp/<branch>/<date>.md) — Reviewer verdict is here under## Reviewer Output.
Follows the programmatic-first principle from AGENTS.md: tasks performed twice interactively must be encoded as scripts.
Workflow & Intentions
1. Confirm Reviewer Approval
Read the ## Reviewer Output section in the session scratchpad. Do not proceed unless the verdict is Approved. If the verdict is Revise or Reject, return to Executive Researcher.
2. Finalise the Document
In the synthesis document (docs/research/<slug>.md):
- Update the
Statusline fromDraft — pending reviewtoFinal. - Add or verify the
Datefield. - Fix any minor formatting issues flagged by the Reviewer (typos, broken links). Do not make substantive edits — those go back to the Synthesizer.
3. Run Synthesis Quality Gate
Before routing to Review, run the programmatic quality gate:
uv run python scripts/validate_synthesis.py docs/research/sources/<slug>.md
# or for D4 issue synthesis:
uv run python scripts/validate_synthesis.py docs/research/<slug>.md
Exit code 0 = gate passed. If exit code 1, do not proceed — enumerate the failures in the scratchpad and return control to the Synthesizer for correction. Do not attempt to fix substantive quality gaps yourself — only status and minor formatting are in scope for the Archivist.
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.
- 3d ago First seen · 172 lines · 27 tokens per session scan A 4c04042f9a37
Research Archivist is an agent published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 9d ago), licensed Apache-2.0. It adds 27 tokens to every session and 1,569 once invoked, about $0.0001 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.
Other agents, from other repositories
github-etiquette
This document provides essential guidelines for AI agents interacting with GitHub issues, pull requests, and comments.
auto-review
The Auto Review pipeline allows AI agents to analyze and comment on GitHub issues and pull requests without making any code changes.
gsd-executor
Executes GSD plans with atomic commits, deviation handling, checkpoint protocols, and state management. Spawned by execute-phase orchestrator or execute-plan command.
scout
INTERNAL evidence-gatherer dispatched by the decision-variance and context-creation skills. Scans an assigned code scope and reports evidence of architectural decisions — file paths and line numbers only. Never dispatch directly.
dependency-reviewer
Dispatched when package.json, lock files, or container base images change. Verifies license, provenance, maintenance signal, and supply-chain posture against .codearbiter/security-controls.md and .codearbiter/tech-stack.md before merge.
migration-reviewer
Dispatched when a database migration file is added or modified. Reviews migrations for safety, data-classification tagging, and immutability against .codearbiter/security-controls.md.