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 spiritledsoftware/caplets --skill writing-capletsgit clone --depth 1 https://github.com/spiritledsoftware/capletsWrote 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/spiritledsoftware/caplets/writing-caplets)<a href="https://agentmods.dev/skills/spiritledsoftware/caplets/writing-caplets"><img src="https://agentmods.dev/badge/skills/spiritledsoftware/caplets/writing-caplets/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/spiritledsoftware/caplets/writing-caplets"><img src="https://agentmods.dev/badge/skills/spiritledsoftware/caplets/writing-caplets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 108 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00064 | $0.01825 |
| Opus 5.5 | $0.00026 | $0.00730 |
| Sonnet 5.5 | $0.00013 | $0.00365 |
| Haiku 4.5 | $0.00006 | $0.00183 |
Grade A, and why
writing-caplets 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 2mo 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Caplets
Core Rule
Write Caplets from the active schema, local conventions, and nearby examples first, not from memory.
Treat the YAML frontmatter as the machine contract and the Markdown body as the agent operating guide. The body should help an agent decide when and how to use the Caplet; it should not read like an installer README.
Before editing, discover the user's environment:
- Find the target Caplet file or intended destination. Common names include
CAPLET.md,caplet.md, files under acaplets/directory, or entries in a Caplet set. - Look for a local schema reference in the file. If none is present, prefer the public schema URL
https://caplets.dev/caplet.schema.jsonwhen adding editor metadata. - Read nearby Caplets, project docs, or config files to match naming, auth, setup, and install conventions.
- Use the repository's own package manager, scripts, and docs for validation when they exist. Do not assume this is the Caplets source repository.
Authoring Workflow
- Clarify the integration goal: what the agent should be able to inspect, search, call, automate, or summarize.
- Pick the backend family that matches the strongest real interface, such as
mcpServer,openapiEndpoint,googleDiscoveryApi,graphqlEndpoint,httpApi,cliTools, orcapletSet.- Prefer OpenAPI, GraphQL, Google Discovery, or explicit HTTP actions when the provider has a stable API contract that Caplets can filter into a compact workflow.
- Prefer MCP when the provider's MCP server is the official curated agent surface or handles workflow/auth better than the raw API.
- Prefer CLI/local backends when the capability is inherently local or project-bound.
- Do not choose MCP only because an MCP server exists.
- Use a multi-backend Caplet file when one provider-scale capability needs several child surfaces under one catalog card and install unit:
- Use plural maps such as
mcpServers,openapiEndpoints,googleDiscoveryApis,graphqlEndpoints,httpApis,cliTools, orcapletSets. - Runtime child handles are
parent__child, based on the file ID and child map key. Users install the parent ID, not the child handle. - Put shared guidance, tags, setup, runtime requirements, and provider-level auth at the parent when they truly apply to every child. Child fields override parent scalars; child auth should stay least-privilege when scopes differ.
- Keep singular
cliTools.actionsfor one CLI backend. In pluralcliTools,actionsis reserved and cannot be a child ID. - Use
capletSetorcapletSetsonly for nesting or composing another Caplets collection, not as the default shape for a single provider suite.
- Use plural maps such as
- Keep checked-in Caplets safe for their audience:
- For public Caplets, never include tokens, credential-bearing URLs, private provider IDs, browser profiles, user home paths, local absolute paths, or account-specific values.
- For private Caplets, still isolate secrets and account-specific values so the Caplet can be reviewed and moved safely.
- Use
$vault:NAMEor${vault:NAME}for secrets the runtime should resolve. Use$env:NAMEonly for non-secret machine-local paths, feature flags, or runtime toggles. - Add
projectBinding.required: truewhen the Caplet reads, searches, executes against, or mutates project files. Explain in the body why the bound project root is required. - Add
setup.commandsandsetup.verifywhen the backend needs local binaries, browser dependencies, generated specs, provider setup, or a repeatable readiness check. - Add
runtime.featuresonly for real runtime requirements such asbrowserordocker. - Add optional
catalog.icononly when it improves public catalog presentation:- Use a safe HTTPS image URL or a bundled image path relative to the Caplet directory.
- Prefer a real provider, project, or capability icon from a license-safe public source when publishing public Caplets.
- Do not use catalog metadata to imply trust, safety, setup readiness, endorsement, or runtime behavior.
- Write the Markdown body for agents that will use the Caplet:
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.
- 2mo ago First seen · 115 lines · 64 tokens per session scan A a21a47db69ec
writing-caplets is a skill published in the GitHub repository spiritledsoftware/caplets (14 stars, last pushed 13d ago), licensed MIT. It adds 64 tokens to every session and 1,825 once invoked, about $0.0003 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 2026-08-30.
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