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/FerroxLabs/ijfwWrote 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/commands/ferroxlabs/ijfw/mode)<a href="https://agentmods.dev/commands/ferroxlabs/ijfw/mode"><img src="https://agentmods.dev/badge/commands/ferroxlabs/ijfw/mode/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/commands/ferroxlabs/ijfw/mode"><img src="https://agentmods.dev/badge/commands/ferroxlabs/ijfw/mode.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 | $0.00022 | $0.00315 |
| Opus 5.5 | $0.00009 | $0.00126 |
| Sonnet 5.5 | $0.00004 | $0.00063 |
| Haiku 4.5 | $0.00002 | $0.00032 |
Grade A, and why
mode 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.
What it actually says
Switch the active IJFW mode. Acknowledge with one line.
smart (default): Auto-routes models, effort, and verbosity by task type. Routine → scout/builder, terse. Complex → architect, thorough. Teaching → normal verbosity.
fast: Maximum efficiency. Cheapest viable model. Low effort. Ultra-terse. Aggressive compaction. For blasting through known tasks.
deep: Maximum quality. Best model. High/max effort. Self-verification on. Plan-then-execute enforced. Normal verbosity. For architecture, security, complex debug.
manual: All IJFW automation off. User controls /model, /effort, verbosity. Output rules still active but overridable. For power users.
brutal: Caveman mode -- 1-sentence answers, code-only, no explanation unless user asks "why" or "explain". Reasoning/thinking preserved, output constrained. Sets IJFW_TERSE_ONLY=1. For blast-mode task execution.
Natural language triggers also work:
- "go fast" / "speed mode" → fast
- "think deeper" / "be thorough" → deep
- "back to normal" / "auto" → smart
- "be brutal" / "caveman mode" / "ultra-terse" → brutal
Mode persists until changed or session ends.
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 · 31 lines · 22 tokens per session scan A 59a42e9b4a06
mode is a command published in the GitHub repository FerroxLabs/ijfw (214 stars, last pushed 2d ago), licensed MIT. It adds 22 tokens to every session and 315 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 2026-09-05.
Other commands, from other repositories
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approve-proposed
Atomically persist every file marked PROPOSED in the most recent prior assistant turn's ### Artifact changes block, promoting staged content to real files. Invoked on request only. Use when that block contained files marked PROPOSED and you have accepted the inline content of each. Do not use when the prior turn had…
stack-currency-check
Verify that the patterns the model will use for a framework and version are current per official docs, and cache it as CURRENTPATTERNS.md for the project. Use when impact-analysis flags greenfield work in an established framework, a new project uses newer-than-cutoff frameworks, or that file is stale (>30 days). Do…
apply-sweep-triage
Persist triage decisions (apply, decline, discuss) into REVIEWPREFERENCES.md so future sweeps suppress declined findings. Use after editing triage values in a SWEEP snapshot from repo-consistency-sweep. Do not use without a snapshot or before triage values are set.
team-update
Write a short, natural English status update for any team channel (Slack, Discord, Teams, email, PR comment, standup notes), grounded and professional. Use when the user wants to share progress with teammates or reviewers, work hits a meaningful checkpoint, or a short async note fits. Do not use when there is no…
crear-skill
Crear, probar y mejorar skills de forma iterativa. Usa cuando el usuario dice "crear skill", "crear habilidad", "build skill", "create a skill", "skill development", "desarrollar skill", "nueva habilidad". Usa el workflow: draft → test → review → improve → repeat con evaluación cuantitativa y qualitative review.