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/omnigent-ai/omnigent/api-docsnpx skills add omnigent-ai/omnigent --skill api-docsgit clone --depth 1 https://github.com/omnigent-ai/omnigentWhat 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 | $0.00050 | $0.00448 |
| Opus 5 | $0.00025 | $0.00224 |
| Sonnet 5 | $0.00010 | $0.00090 |
| Haiku 4.5 | $0.00005 | $0.00045 |
Grade A, and why
api-docs 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 2d 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.
What it actually says
api-docs — document a public API surface from the code
Produce reference docs that match the code exactly, derived from the source, not from assumptions about what the API "probably" does.
Read the surface
Identify the public surface to document (a module, class, CLI command group, or set of endpoints). Then have the researcher read it carefully:
- Dispatch the researcher (
purpose: explore) to enumerate the public entry points and report each one's real signature, parameters, defaults, return shape, and raised errors — with file:line evidence. - Prefer what the code declares (signatures, type hints, docstrings, default
values) over prose descriptions. Public vs. private follows the project's
convention (e.g. a leading underscore, or an
__all__/ export list).
Structure
For each entry point:
### `<name>(<signature>)`
<one-line summary of what it does>
**Parameters**
- `<name>` (`<type>`, default `<value>`) — <meaning>
**Returns** — `<type>`: <meaning>
**Raises** — `<Error>`: <when>
**Example**
```
<minimal, runnable usage>
```
Write the entries
- Keep the summary to one line; put detail in the parameter and example sections.
- Document every public parameter, including defaults, in the order they appear in the signature.
- Give one minimal example per entry point that actually runs against the documented signature.
- Do not document private/internal helpers unless the user asks; a reference is the contract, not a code tour.
Verify
Signatures, defaults, and error types drift fastest, so route the finished
reference through the reviewer (purpose: review) to confirm every signature
and default matches the current code.
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.
- 2d ago First seen · 58 lines · 50 tokens per session scan A fbfcf9b2357d
api-docs is a skill published in the GitHub repository omnigent-ai/omnigent (9,591 stars, last pushed today), licensed Apache-2.0. It adds 50 tokens to every session and 448 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
no-mistakes
Validate your code changes through the no-mistakes pipeline - automated code review, tests, lint, docs, push, PR, and CI - before they reach upstream. Use when the user asks to run no-mistakes, gate or ship or validate their changes, push safely, asks you to do a task and then validate it, or invokes /no-mistakes.
mnemosyne-context
Load this when working on the mnemosyne memory system — its repo, sync server, memory databases, or CI. Covers architecture, the surface/sync data model, dev workflow (tests/ruff/CI matrix), release policy, and known gotchas that are easy to get wrong. Use for any "mnemosyne" dev or devops task, or when a…
hermes-memory-providers
Install and configure Mnemosyne as a Hermes Agent memory provider — local SQLite with vector search, episodic consolidation, and temporal knowledge graphs.
mnemosyne
Persistent cross-session memory via Mnemosyne — store, recall, and consolidate facts, preferences, and context.
autocontext
Iterative strategy generation and evaluation system. Use when the user wants to evaluate agent output quality, run improvement loops, queue tasks for background evaluation, check run status, inspect runtime artifacts and session branch lineage, or discover available scenarios. Provides LLM-based judging with…
autocontext-consumer
Use when an agent needs to USE knowledge Autocontext already produced - find which scenarios have knowledge, read the playbook and lessons for one, understand the on-disk file and folder layout, and move knowledge between checkouts. Host-agnostic; requires only the autoctx CLI and the filesystem.