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 Dataslayer-AI/Marketing-skills --skill ds-evalgit clone --depth 1 https://github.com/Dataslayer-AI/Marketing-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/dataslayer-ai/marketing-skills/ds-eval)<a href="https://agentmods.dev/skills/dataslayer-ai/marketing-skills/ds-eval"><img src="https://agentmods.dev/badge/skills/dataslayer-ai/marketing-skills/ds-eval/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/dataslayer-ai/marketing-skills/ds-eval"><img src="https://agentmods.dev/badge/skills/dataslayer-ai/marketing-skills/ds-eval.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 | $0.00076 | $0.00767 |
| Opus 5 | $0.00038 | $0.00383 |
| Sonnet 5 | $0.00015 | $0.00153 |
| Haiku 4.5 | $0.00008 | $0.00077 |
Grade A, and why
ds-eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Triggering accuracy eval (ds-eval)
You are a QA evaluator for Claude Code skill descriptions. Your job is to determine whether the right skill would trigger for a given user input, based solely on the description field in each skill's frontmatter.
Process
Step 1 — Load test cases and descriptions
Read the test file:
!cat "${CLAUDE_SKILL_DIR}/eval/triggering-tests.yaml" 2>/dev/null || echo "No test file found."
Read all skill descriptions by loading each SKILL.md frontmatter from
the sibling skill directories. Extract only the name and description
fields from each.
If the user passed a filter as argument, only run tests for: $ARGUMENTS
Step 2 — Evaluate each test case
For each test case in the YAML file:
- Read the
inputphrase - Compare it against ALL skill descriptions
- Determine which skill's description is the best match for that input
- Check:
- Does the best match equal
expected_skill? → PASS - Does the best match appear in
should_not_trigger? → FAIL - Is it ambiguous (two descriptions match equally well)? → AMBIGUOUS
- Does the best match equal
Matching criteria — A description "matches" an input when:
- The input contains words or phrases explicitly listed in the description
- The input's intent aligns with the skill's stated purpose
- The description uses "when the user says" followed by a phrase that semantically matches the input
Do NOT match based on:
- General topic overlap (e.g., "organic" doesn't auto-match all SEO skills)
- The body of the SKILL.md — only the description field matters for triggering
Step 3 — Report results
Present results in this format:
Triggering eval results — [date]
Summary: X/Y passed | Z failed | W ambiguous
Passes
| Input | Expected | Matched | Result |
|---|---|---|---|
| ... | ... | ... | PASS |
Failures
For each failure, explain:
- What input was tested
- Which skill was expected
- Which skill matched instead (and why)
- Suggested description edit to fix the mismatch
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 · 107 lines · 76 tokens per session scan A da894744e4ec
ds-eval is a skill published in the GitHub repository Dataslayer-AI/Marketing-skills (22 stars, last pushed 5mo ago), licensed MIT. It adds 76 tokens to every session and 767 once invoked, about $0.0004 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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