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 commands/thierryn/fire-flow/fire-researchgit clone --depth 1 https://github.com/ThierryN/fire-flowWrote this? Show the measurements
A badge for your README with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them.
[](https://agentmods.dev/commands/thierryn/fire-flow/fire-research)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 | $0.00003 | $0.03027 |
| Opus 5 | $0.00002 | $0.01514 |
| Sonnet 5 | $0.00001 | $0.00605 |
| Haiku 4.5 | $0.00000 | $0.00303 |
Grade A, and why
fire-research 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 — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/fire-research
Daily AI research-to-upgrade pipeline — search papers, score findings, implement in breaths
Purpose
Systematic daily workflow for finding, evaluating, and implementing AI research improvements into dominion-flow. Eliminates the need to explain the process each session — any fresh agent follows this pipeline autonomously.
Proven track record: 7 successful executions (v3.2 through v8.0), producing 70+ implemented enhancements from 200+ papers analyzed.
Arguments
arguments:
focus:
required: false
type: string
description: "Research focus area (e.g., 'memory consolidation', 'failure taxonomy')"
example: "/fire-research agent confidence calibration"
optional_flags:
--scan-only: "Research and score papers but don't implement (reconnaissance mode)"
--from-deferred: "Start from deferred candidates of last version"
--quick: "Single agent search instead of parallel (faster, less thorough)"
Process
Step 1: Load the Research Pipeline Skill
Read the full methodology skill — it contains scoring matrices, wave templates, and citation formats refined across 7 versions:
@skills-library/methodology/RESEARCH_BACKED_WORKFLOW_UPGRADE.md
This is MANDATORY. The skill contains the paper scoring matrix, gap taxonomy, wave grouping rules, and citation format. Do not skip.
Step 2: Determine Focus Area
If arguments provided: Use as focus area directly.
If --from-deferred: Check MEMORY.md and recent handoffs for deferred candidates:
cd ~/.claude/memory
npm run search -- "deferred candidate" --type handoff --limit 5
If no arguments: Ask user:
What area should we research today?
Recent deferred candidates from previous versions:
1. [List from memory search]
2. [List from memory search]
Or describe a new focus area:
> [User input]
Step 3: Launch Parallel Research Agents
Launch 3-4 agents in a SINGLE message for true parallelism:
Agent 1: Academic Papers (arXiv, ACL, NeurIPS, ICML, ICLR — 2024-2026)
- Search: "[focus area] AI agent 2025 2026"
- Extract per paper: title, venue, date, key finding, measurable result,
how it maps to our workflow, specific file/step it would modify
- Return: Top 15 papers ranked by APPLICABILITY
Agent 2: Community Patterns + Industry Tools
- Search: Manus AI, Replit Agent, Cursor, Devin, SWE-Agent, Claude Code
- Focus: What do production AI coding tools do that we don't?
- Return: Top 10 patterns with source links
Agent 3: Internal Gap Analysis (Explore subagent)
- Read: All workflow files in the dominion-flow plugin directory
- Classify each gap: MEMORY | REFLECTION | PLANNING | ACTION | SYSTEM
- Return: Top 10 gaps ranked by impact
Agent 4 (optional): Failure Pattern Mining
- Search Qdrant: debug_resolution + failure_pattern source types
- Find recurring failures indicating systemic gaps
- Return: Top 5 failure patterns
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 · 371 lines · 3 tokens per session scan A fe6dbdf37b59
fire-research is a command published in the GitHub repository ThierryN/fire-flow (77 stars, last pushed 21d ago), licensed MIT. It adds 3 tokens to every session and 3,027 once invoked, about $0.0000 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 2026-08-30.
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data-scientist
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audit-prompt
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update-prompt
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labs
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ads_canonical-models-review
Data team review of canonical model implementation.