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 opendatahub-io/ai-helpers --skill doc-gapgit clone --depth 1 https://github.com/opendatahub-io/ai-helpersWrote 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/opendatahub-io/ai-helpers/doc-gap)<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/doc-gap"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/doc-gap/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/opendatahub-io/ai-helpers/doc-gap"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/doc-gap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Privilege Escalation · line 60 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Excessive Agency · line 8 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00041 | $0.01078 |
| Opus 5 | $0.00020 | $0.00539 |
| Sonnet 5 | $0.00008 | $0.00216 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
doc-gap 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 10d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
doc-gap
Assess whether the gathered context is sufficient to produce quality documentation.
Prerequisites
workspace/context-package.json must exist (produced by doc-gather).
Parse arguments
$ARGUMENTS optionally contains a component name to focus the analysis on. If empty, analyze all components found in the context package.
Input validation:
- If a component name is provided, validate it against the component list in
workspace/context-package.json. - Accept only exact matches against known component names.
- If the component name is not found, halt with a clear error and do not run LLM assessment.
Step 1: Read context package
Read workspace/context-package.json and extract:
- Ticket metadata (summary, components, fix_versions)
- List of gathered context files with their source types
- Product configuration (docs conventions)
Step 2: Deterministic coverage checks
Perform these checks without LLM judgment:
- Component coverage: For each component in the ticket, check if at least one context file from that component's repo exists.
- Documentation existence: Check if existing documentation files are present in the context.
- API reference availability: If the ticket involves API changes, check for CRD type definitions or API spec files.
- Source code presence: Check if implementation source code is included.
- Architecture docs: Check for architecture context files.
Record each check as a finding with pass/fail status.
Step 3: LLM assessment
Read the gap analysis prompt from ${CLAUDE_SKILL_DIR}/prompts/gap-analysis.md.
Construct an LLM prompt combining:
- The gap analysis prompt template
- Ticket metadata from the context package
- Summary of gathered files (file paths, source types, relevance scores)
- Content snippets from the highest-scored files (first 500 chars each, up to 20 files), after deterministic secret/PII redaction
- Results of deterministic checks from Step 2
Before prompt assembly, apply deterministic redaction to all snippets:
- Detect and mask secrets (API keys, tokens, passwords, private keys, kubeconfig credentials) with consistent placeholders (e.g.,
<REDACTED_TOKEN_1>). - Mask PII fields (emails, phone numbers) unless explicitly required.
- Record redaction statistics (number of snippets scanned, items redacted by type) for inclusion in the gap report.
What ships with it
1 file 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.
- 10d ago First seen · 125 lines · 41 tokens per session scan A 0b5b26a88c65
doc-gap is a skill published in the GitHub repository opendatahub-io/ai-helpers (37 stars, last pushed 3d ago), licensed Apache-2.0. It adds 41 tokens to every session and 1,078 once invoked, about $0.0002 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.
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