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/autofix-skills --skill autofix-repo-resolvegit clone --depth 1 https://github.com/opendatahub-io/autofix-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/opendatahub-io/autofix-skills/autofix-repo-resolve)<a href="https://agentmods.dev/skills/opendatahub-io/autofix-skills/autofix-repo-resolve"><img src="https://agentmods.dev/badge/skills/opendatahub-io/autofix-skills/autofix-repo-resolve/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/autofix-skills/autofix-repo-resolve"><img src="https://agentmods.dev/badge/skills/opendatahub-io/autofix-skills/autofix-repo-resolve.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00049 | $0.00956 |
| Opus 5 | $0.00024 | $0.00478 |
| Sonnet 5 | $0.00010 | $0.00191 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
autofix-repo-resolve 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 12d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Repo URL Disambiguation
You are resolving which repository is the actual target of a Jira bug ticket that mentions multiple repository URLs. Your goal is to determine which single repo the bug should be fixed in.
Step 1: Read inputs
- Read
.autofix-context/ticket.jsonfor the ticket description, comments, and Jira component name. - Read
.autofix-context/repo-candidates.jsonfor the list of candidate repository URLs detected in the ticket.
Step 2: Analyze the ticket context
For each candidate URL, classify it by reading the surrounding context in the ticket description and comments:
- target: The repo the bug is actually about. Look for:
- Explicit statements: "the impacted repository is...", "target repo:", "affected repo:", "this is a bug in..."
- The repo discussed alongside the actual symptoms, error messages, or stack traces
- The repo that matches the Jira component name
- contextual: Mentioned for background but not the target. Look for:
- Cross-references: "see also", "related to", "similar issue in..."
- Reproduction environments, test fixtures, or demo repos
- Repos mentioned in steps-to-reproduce as tools or dependencies, not as the thing that's broken
- negative: Explicitly stated as NOT the problem. Look for:
- "this is NOT a X issue", "not related to Y", "X is functioning correctly"
- "removed X component", "not a code issue in Y"
- unclear: Cannot determine the relationship from context.
Step 3: Determine target and confidence
- high: One URL has an explicit target statement, or only one URL remains after filtering out negative and contextual mentions.
- medium: One URL is more likely the target based on proximity to bug symptoms, but no explicit statement. Or, the Jira component name aligns with one candidate.
- low: Multiple URLs remain plausible targets after analysis. No clear signal.
If confidence is low, set target_url to the most likely candidate but note the ambiguity in reasoning.
What ships with it
3 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.
- 12d ago First seen · 83 lines · 49 tokens per session scan A 3fa281ddaba1
autofix-repo-resolve is a skill published in the GitHub repository opendatahub-io/autofix-skills (2 stars, last pushed 12d ago), licensed Apache-2.0. It adds 49 tokens to every session and 956 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 2026-08-31.
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