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 redhat-community-ai-tools/harness-eval --skill eval-skillgit clone --depth 1 https://github.com/redhat-community-ai-tools/harness-evalWrote 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/redhat-community-ai-tools/harness-eval/eval-skill)<a href="https://agentmods.dev/skills/redhat-community-ai-tools/harness-eval/eval-skill"><img src="https://agentmods.dev/badge/skills/redhat-community-ai-tools/harness-eval/eval-skill.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 5.1 | $0.00049 | $0.00927 |
| Opus 5 | $0.00024 | $0.00464 |
| Sonnet 5 | $0.00010 | $0.00185 |
| Haiku 4.5 | $0.00005 | $0.00093 |
Grade C, and why
eval-skill scanned grade C with 1 finding 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 8d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- evaluator-ignore: content/broken-references, content/allowed-tools-auto-approve --> How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate Skill
Deep-evaluate a single skill using lint (deterministic rules) and qualitative review, both individually and in context of the full setup.
Hard Rules
- Never give a verdict without running the checks. Read the actual file content and check all rubric categories before assigning a verdict.
- Every category must be checked. Both the individual rubric AND the contextual analysis must be fully evaluated.
- Read before you judge. Read the actual SKILL.md content (and reference files if they exist).
- Don't manufacture problems. If the skill is good, say so. Only report real issues.
- Always end with a short summary.
- Record the exact start time and compute the exact duration at the end.
Step 1: Ask Output Preference
Before doing anything else, ask the user:
Where should i present the results?
- Terminal - print the report here in the conversation
- File - write a markdown report to a file (you'll choose the path)
Wait for their answer before proceeding.
Step 2: Select the Skill
Determine the skill path. If the user says a skill name, find it under skills/<name>/SKILL.md.
Step 3: Run Lint (Static Analysis)
Determine the setup context path (usually the current working directory).
uvx --from harness-eval harness-eval skill-review <skill-path> --context <context-path> --format json
If uvx is not available, fall back to pip install harness-eval and use harness-eval directly.
If no context path, omit the --context flag.
Read the JSON output. It contains diagnostics, token count, and contextual findings. This runs the recommended preset (quality + structural rules). For full security vetting, use /skill-verify instead.
Step 4: Read Actual Files
Read the skill's actual content:
- The SKILL.md file
- All files in the skill's subdirectories (reference files). Check the COMBINED content.
- The skill's guidelines.md (if it exists)
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.
- 8d ago First seen · 102 lines · 49 tokens per session scan C 2a0f82cbdd55
eval-skill is a skill published in the GitHub repository redhat-community-ai-tools/harness-eval (27 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 927 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
code-review
Paranoid architect review of code changes for bugs, security, missing tests, and undocumented assumptions. Works on local git diffs OR a GitHub pull request (e.g. owner/repo N). For PRs, can post findings as line-level review comments.
parallel-pr-review
Use when asked to "review the open PRs", review a batch or stack of pull requests, or run a recurring PR-review pass on a repo — especially with many PRs, stacked branches, conflicts, or security-sensitive changes. Covers grouping, fan-out to review subagents, verdict synthesis, and posting.
qa-review
QA review for code changes — test coverage analysis, edge case identification, test plan generation, regression detection, test health tracking over time.
security-review
Security audit for code changes and PRs — OWASP top 10, auth flows, data handling, secrets exposure, supply chain risks. Writes findings as actionable items.
review-readiness
PR readiness dashboard — tracks which reviews have been completed per branch and gates merge decisions. Shows code review, tests, security, QA, and linting status.
review-checklist
Pre-merge review checklist based on recurring AI reviewer feedback patterns.