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/petekp/claude-code-setup/latent-potentialnpx skills add petekp/claude-code-setup --skill latent-potentialgit clone --depth 1 https://github.com/petekp/claude-code-setupWrote 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/petekp/claude-code-setup/latent-potential)<a href="https://agentmods.dev/skills/petekp/claude-code-setup/latent-potential"><img src="https://agentmods.dev/badge/skills/petekp/claude-code-setup/latent-potential.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 | $0.00191 | $0.03126 |
| Opus 5 | $0.00096 | $0.01563 |
| Sonnet 5 | $0.00038 | $0.00625 |
| Haiku 4.5 | $0.00019 | $0.00313 |
Grade A, and why
latent-potential 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 today.
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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Latent Potential
Walk into the project the way a hand-picked expert team would: a strategist, a positioning specialist, a product thinker, an architect, a distribution person, an ecosystem watcher, and a skeptic. The job is not to review the code. The job is to find value that is already latent in the project, name it precisely, and argue for the few moves that would channel it into something genuinely valuable and impactful.
"Latent" is the operative word. The best outcome of this exercise is not a clever idea the maintainer could chase. It is the discovery that they are already most of the way to something they have not noticed. Every recommendation should trace back to an asset that already exists.
Principles
-
Mechanisms, not labels. Describe the project by what it actually does, not by its category. "A note-taking app" invites note-taking-app ideas. "A local-first sync engine with a markdown editor bolted on" invites uses no label would suggest, and reveals that the sync engine might be the product. First principles here means re-deriving what the thing is from its parts, then reasoning from that.
-
Latent means already present. Every opportunity must point at a specific existing asset: code, data, docs, tests, tooling, a published package, a domain, an audience, accumulated know-how. Apply the 90% test: is the project already most of the way there without realizing it? If a recommendation requires building the core asset from scratch, it is a generic startup idea, not latent potential. Cut it or flag it as out of scope.
-
Lateral before vertical. Sweep wide first. Generate many small hypotheses across every domain (strategy, positioning, product, architecture, distribution, timing) before developing any of them. The first good idea is a trap: it anchors every thought after it. Depth comes later, and only for survivors.
-
Evidence or it does not ship. Cite real files, real commits, real docs. The anti-generic test for every recommendation: could a smart consultant have written this sentence without reading the repo? If yes, it does not belong in the report.
What ships with it
2 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.
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.
- today Changed · +191 tokens per session ba996e25736f
- 4d ago First seen · 156 lines · 0 tokens per session scan A faab96018c53
latent-potential is a skill published in the GitHub repository petekp/claude-code-setup (47 stars, last pushed today), licensed MIT. It adds 191 tokens to every session and 3,126 once invoked, about $0.0010 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 2026-08-31.
Other skills, from other repositories
html-artifacts
Author the HTML for a plan artifact, dashboard iframe, or Slack attachment — structure, design plan, available runtime, theming, and craft. Read this before writing HTML for saveplan, outputiframe, or slackattachhtml.
bootstrap-repo-analysis
First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the…
baby-sit
Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.
write-oep
Draft an Open SWE Enhancement Proposal using the repository's OEP process and template. Use when the user asks to create, write, or propose an OEP or invokes /write-oep.
continual-learning
Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt…
AI & LLM Security
LLM and AI application security testing — prompt injection, jailbreak resistance, OWASP LLM Top 10 (2025), RAG and agent/tool-use security, model supply chain, and AI red teaming for authorized assessments.