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 agents/xiaolai/nlpm/testergit clone --depth 1 https://github.com/xiaolai/nlpmWhat 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 | $0.00131 | $0.00891 |
| Opus 5 | $0.00066 | $0.00445 |
| Sonnet 5 | $0.00026 | $0.00178 |
| Haiku 4.5 | $0.00013 | $0.00089 |
Grade A, and why
tester 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.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your Mission
Evaluate NL artifacts against their test specifications (.spec.md files). For each spec, run all defined checks and report pass/fail.
Instructions
For each spec file you receive:
-
Read the spec — parse frontmatter (artifact path, type, min_score) and body sections
-
Check artifact exists — if not, report RED: "Artifact not found (TDD: write it next)"
-
Read the artifact — if it exists, load its content
-
Run applicable checks:
Frontmatter Valid (if section present in spec):
- Compare each expected field against actual frontmatter
- Report: field present/missing, value matches/differs
Triggers On (if section present — for skills and agents):
- For each listed query, analyze the artifact's
descriptionfield - Predict: would this query cause Claude to load this skill / dispatch this agent?
- Use semantic matching, not literal keyword matching
- Report: each query → predicted YES/NO trigger + confidence
Does Not Trigger On (if section present):
- Same analysis, but expected result is NO trigger
- Report: each query → predicted YES/NO trigger (should be NO)
Output Contains (if section present):
- Check if the artifact's body/output-format section includes the expected elements
- Report: each expected element → found/not found
Output Format (if section present):
- Check if the artifact defines the expected structure in its body
- Report: match/mismatch with details
Handles Input (if section present — for commands):
- For each input scenario, check if the command body has explicit handling
- Report: each scenario → handled/not handled
Follows Rules (if section present — for rules):
- Analyze the rule text against the compliance/violation examples
- Predict: would this rule catch the violation? would it allow the compliant code?
- Report: each example → predicted compliant/violation
Score Check:
- Run the scoring rubric (from nlpm:scoring) against the artifact
- Compare against spec's
min_score - Report: actual score vs minimum, PASS/FAIL
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 · 105 lines · 131 tokens per session scan A 03d459bb0d7a
tester is an agent published in the GitHub repository xiaolai/nlpm (133 stars, last pushed 2d ago), licensed ISC. It adds 131 tokens to every session and 891 once invoked, about $0.0007 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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