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 skills/tobiasblask/open-paper-machine/positioning-enginenpx skills add TobiasBlask/open-paper-machine --skill positioning-enginegit clone --depth 1 https://github.com/TobiasBlask/open-paper-machineWrote 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/skills/tobiasblask/open-paper-machine/positioning-engine)<a href="https://agentmods.dev/skills/tobiasblask/open-paper-machine/positioning-engine"><img src="https://agentmods.dev/badge/skills/tobiasblask/open-paper-machine/positioning-engine.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 5.1 | $0.00059 | $0.02386 |
| Opus 5 | $0.00030 | $0.01193 |
| Sonnet 5 | $0.00012 | $0.00477 |
| Haiku 4.5 | $0.00006 | $0.00239 |
Grade A, and why
positioning-engine 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 6d 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 — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestration Log: When this skill is activated, append a log entry to
outputs/orchestration_log.md:### Skill Activation: Positioning Engine **Timestamp:** [current date/time] **Actor:** AI Agent (positioning-engine) **Input:** Paper draft + [N] comparison papers identified **Output:** Differentiation matrix with [N] dimensions, positioning_analysis.md saved
Positioning Engine
Core Principle
"How is your paper different from X?" is the question every reviewer asks. This engine produces a systematic answer. It identifies the 5-10 most similar existing papers, builds a structured differentiation matrix, and generates a positioning statement that makes the unique contribution explicit and defensible.
The output directly strengthens the Introduction (gap + contribution paragraphs) and the Discussion (theoretical implications).
When to Activate
- User says "position my paper", "how is this different from X?", "differentiation"
- User says "compare to related work", "positioning analysis", "unique contribution"
- During Phase 2 (Framing) to sharpen the gap and contribution
- When a reviewer challenges the novelty or contribution
- User runs
/analyze-positioning
Prerequisites
draft.mdorpaper.texexists (to understand the paper's claims)references.biband/orliterature_base.csvexist (comparison candidates)- Research questions and contribution are at least tentatively defined
Step 1: IDENTIFY Closest Competitors
Finding the Most Similar Papers
Sources for comparison papers:
- From the paper itself: Papers cited in the Introduction and Related Work that address the same or very similar research questions
- From
literature_base.csv: Papers with the highest topical overlap - From snowballing: Forward citations of the paper's key references that appeared after those references were published
- Direct search: Search for papers with very similar titles or identical keywords
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.
- 6d ago First seen · 287 lines · 59 tokens per session scan A 559ee8f31d69
positioning-engine is a skill published in the GitHub repository TobiasBlask/open-paper-machine (18 stars, last pushed 4mo ago), licensed MIT. It adds 59 tokens to every session and 2,386 once invoked, about $0.0003 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.
Other skills, from other repositories
meta-tags-optimizer
Optimize title tags, meta descriptions, Open Graph, and Twitter cards for maximum click-through rate. Generates multiple A/B test variations with character counting and SERP preview. Use when asked to "optimize title tag", "write meta description", "improve CTR", "Open Graph tags", "fix my meta tags", "social media…
google-ads-audit
Google Ads account audit and business context setup. Run this first — it gathers business information, analyzes account health, and saves context that all other ads skills reuse. Trigger on "audit my ads", "ads audit", "set up my ads", "onboard", "account overview", "how's my account", "ads health check", "what should…
sxo
Search Experience Optimization (SXO) — the bridge between SEO and UX/CRO. Audits the full journey from the SERP click to the on-page goal: SERP click-through factors (title/meta/rich results that win the click), then post-click experience signals that keep users and drive conversions — above-the-fold relevance and…
elixir-idioms
OTP/BEAM patterns and Elixir idioms — GenServer, Supervisor, Task, Registry, pattern matching, with chains, pipes. Use when designing processes or debugging BEAM issues.
security
Enforce Elixir/Phoenix security — auth, OAuth, sessions, CSRF, XSS, SQL injection, input validation, secrets. Use when editing auth files, login flows, RBAC, or API keys.
tidewave-integration
Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.