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 skills add pproenca/dot-skills --skill marketplace-pre-member-personalisationgit clone --depth 1 https://github.com/pproenca/dot-skillsWrote 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/pproenca/dot-skills/marketplace-pre-member-personalisation)<a href="https://agentmods.dev/skills/pproenca/dot-skills/marketplace-pre-member-personalisation"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/marketplace-pre-member-personalisation/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/skills/pproenca/dot-skills/marketplace-pre-member-personalisation"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/marketplace-pre-member-personalisation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00173 | $0.03290 |
| Opus 5 | $0.00086 | $0.01645 |
| Sonnet 5 | $0.00035 | $0.00658 |
| Haiku 4.5 | $0.00017 | $0.00329 |
Grade A, and why
marketplace-pre-member-personalisation 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 7d 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketplace Engineering Two-Sided Pre-Member Personalisation Best Practices
Comprehensive design and diagnostic guide for the pre-member journey of a two-sided trust marketplace. Covers anonymous signal inference, side-specific validation (what pet owners and pet sitters each need to see before paying), information-asymmetry closure, progressive profile building, social proof, conversion psychology, onboarding intent capture, identity stitching, and pre-member measurement. Contains 53 rules across 10 categories, ordered by cascade impact, every rule grounded in published consumer-trust and decision research.
When to Apply
Reference this skill when:
- Designing or reviewing the anonymous landing page and first-render experience
- Choosing what to show a visitor before they have registered or paid
- Designing the onboarding flow and deciding which questions to ask in what order
- Planning the paywall moment — timing, copy, triggers, price anchoring
- Diagnosing a conversion funnel that is leaking between visit and paid membership
- Choosing how to persist visitor state across the anonymous → registered → member transition
- Measuring pre-member experiments and deciding whether to ship an intervention
- Answering "what does a pet owner or sitter actually need to believe before paying?"
This skill is the precursor to marketplace-personalisation and
marketplace-search-recsys-planning. Start here for anything pre-paid-membership;
hand off to those two skills at the paid-member boundary.
Research foundations
Every rule in this skill is grounded in published research on consumer trust, decision-making under risk, marketplace economics, and experimentation:
| Research source | What it informs |
|---|---|
| Cialdini — Influence | Social proof (specific beats aggregate), similarity principle, commitment |
| Kahneman & Tversky — Prospect Theory | Loss aversion, price anchoring, risk framing |
| Roth — Who Gets What and Why | Matching-market dynamics, two-sided acceptance rates, cold-start penalty |
| Fogg — Behavior Model | Motivation × ability × trigger, paywall timing |
| Bandura — Self-Efficacy Theory | First-stay path design, concrete-step persuasion |
| Slovic — Affect Heuristic | Risk overweighting, safety-signal prominence |
| Nielsen Norman Group | Form design, trust, review credibility |
| Trope & Liberman — Construal Level Theory | Psychological distance, local proof |
| Ein-Gar, Shiv, Tormala — Blemishing Effect | Mixed-review credibility |
| Small & Loewenstein — Identifiable Victim Effect | Named-person vs statistic evidence |
| Green & Brock — Narrative Transportation | First-experience stories |
| Kohavi — Trustworthy Online Experiments | Primary outcomes, proxy metrics, segmentation |
| Radlinski & Craswell — Optimized Interleaving | Fast ranking experiments |
| Airbnb / DoorDash engineering | Two-sided marketplace ranking and search |
What ships with it
59 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- AGENTS.md 16 KB
- assets/templates/_template.md 2.5 KB
- gotchas.md 1.5 KB
- metadata.json 2.9 KB
- README.md 6.6 KB
- references/_sections.md 4.5 KB
- references/convert-anchor-price-against-local-alternative.md 2.1 KB
- references/convert-never-interrupt-active-search.md 2.0 KB
- references/convert-re-engage-non-converting-registrants-personalised.md 2.7 KB
- references/convert-trigger-paywall-on-specific-listings.md 2.2 KB
- references/convert-use-loss-aversion-framing-on-soft-locks.md 2.1 KB
- references/gap-display-acceptance-rate-for-profile-shape.md 2.2 KB
- references/gap-link-to-realistic-first-experience-story.md 2.3 KB
- references/gap-route-unworkable-segments-to-alternatives.md 2.2 KB
- references/gap-surface-lead-time-reality.md 2.3 KB
- references/gap-surface-seasonal-supply-constraints.md 2.2 KB
- references/gap-warn-about-cold-start-penalty.md 2.2 KB
- references/measure-attribute-conversion-to-signal-change.md 2.0 KB
- references/measure-define-anonymous-to-member-as-primary-outcome.md 1.8 KB
- references/measure-run-interleaving-for-fast-experiments.md 2.2 KB
- references/measure-segment-by-channel-and-visitor-profile.md 2.3 KB
- references/onboard-allow-answer-revision-without-restart.md 2.0 KB
- references/onboard-ask-highest-information-gain-first.md 1.9 KB
- references/onboard-ask-role-before-anything-else.md 1.9 KB
- references/onboard-make-optional-questions-genuinely-skippable.md 1.9 KB
- references/onboard-prefill-from-inferred-signal.md 1.9 KB
- references/owner-anchor-cost-against-local-alternative.md 1.9 KB
- references/owner-demystify-effort-explicitly.md 2.3 KB
- references/owner-display-honest-local-availability.md 2.2 KB
- references/owner-rank-sitters-by-pet-match-experience.md 2.0 KB
- references/owner-show-specific-local-reviews.md 2.0 KB
- references/owner-surface-safety-guarantees-prominently.md 2.1 KB
- references/profile-build-incrementally-on-each-interaction.md 2.2 KB
- references/profile-decay-features-with-inactivity.md 1.9 KB
- references/profile-persist-across-tabs-and-reloads.md 1.9 KB
- references/profile-reset-on-explicit-role-change.md 2.1 KB
- references/profile-surface-confidence-alongside-predictions.md 2.2 KB
- references/proof-localise-social-proof-to-visitor-area.md 1.9 KB
- references/proof-match-peer-stories-to-inferred-cohort.md 1.9 KB
- references/proof-source-stories-from-real-history-not-handpicked.md 2.3 KB
- references/proof-surface-mixed-reviews-not-only-five-star.md 1.7 KB
- references/proof-use-specific-peer-stories-not-aggregates.md 1.9 KB
- references/signal-capture-entry-point-metadata.md 1.9 KB
- references/signal-classify-inbound-intent.md 2.4 KB
- references/signal-extract-role-from-url-and-referrer.md 1.9 KB
- references/signal-infer-geography-with-confidence.md 2.0 KB
- references/signal-separate-raw-from-derived.md 2.1 KB
- references/signal-use-anonymous-session-tokens.md 1.9 KB
- references/sitter-be-honest-about-first-stay-competition.md 2.3 KB
- references/sitter-disclose-hidden-costs-transparently.md 2.4 KB
- references/sitter-provide-concrete-first-stay-path.md 2.4 KB
- references/sitter-rank-stays-by-travel-goal.md 2.1 KB
- references/sitter-show-inventory-in-target-destinations.md 2.3 KB
- references/sitter-show-typical-daily-commitment.md 2.1 KB
- references/stitch-avoid-cross-contamination-on-account-switch.md 1.9 KB
- references/stitch-degrade-gracefully-on-low-confidence.md 2.2 KB
- references/stitch-handle-multi-device-via-privacy-safe-signal.md 2.1 KB
- references/stitch-preserve-profile-across-registration.md 2.1 KB
- references/stitch-use-deterministic-matching-for-returning-visitors.md 1.9 KB
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.
- 7d ago First seen · 187 lines · 173 tokens per session scan A 97f4c4c08a87
marketplace-pre-member-personalisation is a skill published in the GitHub repository pproenca/dot-skills (207 stars, last pushed 26d ago), licensed MIT. It adds 173 tokens to every session and 3,290 once invoked, about $0.0009 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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