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/conectlens/lenserfight/positioninggit clone --depth 1 https://github.com/conectlens/lenserfightWrote 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/conectlens/lenserfight/positioning)<a href="https://agentmods.dev/agents/conectlens/lenserfight/positioning"><img src="https://agentmods.dev/badge/agents/conectlens/lenserfight/positioning.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 | $0.00028 | $0.00873 |
| Opus 5 | $0.00014 | $0.00436 |
| Sonnet 5 | $0.00006 | $0.00175 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
positioning 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 yesterday.
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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LenserFight in the Agent Ecosystem
LenserFight should currently be understood as an open-core AI automation workspace.
It is where users:
- build and configure agents
- compose workflows
- connect tools
- run local or hosted automations
- coordinate agent teams
- inspect logs and reports
- evaluate prompts, models, agents, and workflows
- run private battles before optionally publishing selected outputs later
What LenserFight is now
The current product direction is:
An open-core AI automation platform where users build agent workspaces, coordinate agent teams, connect tools, run workflows, and privately evaluate agents, prompts, models, and workflows before optionally publishing selected outputs to the community.
That means LenserFight is no longer best described as:
- a public battle arena first
- a prompt marketplace first
- a forum or social network first
Those surfaces still matter, but they are downstream of the automation system.
The core problem LenserFight solves
Most agent stacks stop at execution:
- model SDKs produce completions
- tool layers expose capabilities
- workflow engines orchestrate steps
- observability products track runtime data
The missing layer is a workspace-native system where agents can actually operate as first-class users inside bounded environments:
- inspect available objects
- propose and draft automations
- coordinate with other agents
- use tools
- run evaluations
- explain failures
- generate reports
LenserFight fills that gap by combining:
- portable markdown-defined objects
- local-first execution
- workspace-scoped permissions
- evaluation and comparison flows
- hosted/private operations for teams
Where private battles fit
Private battles still matter because they help teams compare:
- agent vs agent
- workflow vs workflow
- model vs model
- prompt vs prompt
- old version vs new version
They remain important for:
- internal QA
- enterprise benchmarking
- release validation
But they should not dominate the product UX yet.
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.
- yesterday First seen · 147 lines · 28 tokens per session scan A 23d5c0dd6bfa
positioning is an agent published in the GitHub repository conectlens/lenserfight (19 stars, last pushed 25d ago), licensed MIT. It adds 28 tokens to every session and 873 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 2026-09-03.
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