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 nthnclrk/enablement-skills --skill discovery-rubric-auditorgit clone --depth 1 https://github.com/nthnclrk/enablement-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/nthnclrk/enablement-skills/discovery-rubric-auditor)<a href="https://agentmods.dev/skills/nthnclrk/enablement-skills/discovery-rubric-auditor"><img src="https://agentmods.dev/badge/skills/nthnclrk/enablement-skills/discovery-rubric-auditor/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/nthnclrk/enablement-skills/discovery-rubric-auditor"><img src="https://agentmods.dev/badge/skills/nthnclrk/enablement-skills/discovery-rubric-auditor.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 | $0.00090 | $0.00931 |
| Opus 5 | $0.00045 | $0.00465 |
| Sonnet 5 | $0.00018 | $0.00186 |
| Haiku 4.5 | $0.00009 | $0.00093 |
Grade A, and why
discovery-rubric-auditor 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discovery rubric auditor
Score the pattern, not the one-off. If the finding could be one bad call, do not coach the team on it.
An audit needs a comparable sample, one rubric, and a few recurring gaps. One recorded call is call-review-coach. A pass/fail readiness decision is certification-program-builder.
Pattern. "In 7 of 9 mid-market first calls, reps named a problem and never asked what it costs. Coach the cost question next week. Do not rank the AE with two calls."
One-off. "The team needs better discovery. AE3 scored 2.1. Roll out a discovery refresh."
If you cannot say how often the gap appeared, in which comparable slice, it is not a pattern.
Confirm Inputs First
Ask only for the inputs that change the audit:
- The sample and why these calls. One call: route to
call-review-coach - Comparison units: role, segment, stage, call type, time window
- Source coverage: recordings, transcripts, notes, or summaries
- Rubric source, or use the reference
- Reviewer count and whether they already calibrated
- The decision this audit must support: coaching priority or baseline, not certification
Continue from a thin sample with the limits named. Do not invent a leaderboard.
Read The Right Reference
Read references/discovery-rubric-framework.md for anchors, calibration, and when a number is only directional. Skip it if the user already supplied a calibrated rubric and only wants the pattern read.
Default Workflow
- Frame the sample. Separate unlike roles, segments, stages, or call types. Done when each reported unit is comparable.
- Calibrate, or label the single-reviewer path.
Agree anchors on benchmark calls first. One reviewer: delayed blind rescore and
inter-rater reliability: not established. Done when disagreements greater than one point are resolved or named. - Score with evidence.
Every dimension gets a score or
N/O, coverage, and confidence. Silence in missing audio is not a1. Done when call-level evidence can be inspected. - Check drift. Double-score a subset, or blindly rescore after a delay. Pause aggregation if anchors still disagree.
- Aggregate only comparable units.
Count, median, distribution,
N/Orate, coverage. Every number keeps its denominator. Fewer than three comparable calls per unit is directional. - Separate pattern from one-off. Recurring clusters only. Frequency is not causation. Skill gap, process gap, and sample-shape issue are different findings.
- Pick a few interventions. What managers inspect in the next call sample. Done when each action has a behavior and a slice.
What ships with it
2 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 · 79 lines · 90 tokens per session scan A 22b0a3c34af7
discovery-rubric-auditor is a skill published in the GitHub repository nthnclrk/enablement-skills (13 stars, last pushed 23d ago), licensed MIT. It adds 90 tokens to every session and 931 once invoked, about $0.0005 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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