Borrowing it
Nothing to install: this file belongs to zkysar1/Claude-Mind. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zkysar1/Claude-Mind/main/.claude/skills/verify-learning-staleness/SKILL.mdgit clone --depth 1 https://github.com/zkysar1/Claude-MindWrote 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/zkysar1/claude-mind/verify-learning-staleness)<a href="https://agentmods.dev/skills/zkysar1/claude-mind/verify-learning-staleness"><img src="https://agentmods.dev/badge/skills/zkysar1/claude-mind/verify-learning-staleness/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/zkysar1/claude-mind/verify-learning-staleness"><img src="https://agentmods.dev/badge/skills/zkysar1/claude-mind/verify-learning-staleness.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 | $0.00195 | $0.01614 |
| Opus 5 | $0.00097 | $0.00807 |
| Sonnet 5 | $0.00039 | $0.00323 |
| Haiku 4.5 | $0.00019 | $0.00161 |
Grade A, and why
verify-learning-staleness 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/verify-learning-staleness — Verify-Learning Staleness Scanner
Detect stale Check: and Bash: assertions in verify-learning/SKILL.md
whose underlying targets (paths, phase headers, grep patterns) have been
moved, renamed, or extracted out of the codebase without the check being
updated.
Why This Exists
verify-learning/SKILL.md accumulates ~2000 verification assertions
over time. Each assertion references a concrete artifact: a script
path, a SKILL.md phase number, a grep pattern in a named file. When
refactors happen — extracting inline pseudocode into Python, renumbering
phases, deleting retired scripts — the verification check goes stale
silently. Future /verify-learning runs report PASS for stale checks
because the assertion can't even be evaluated against current code,
producing false confidence.
This skill is the structural complement: it scans every assertion and reports which ones reference artifacts the codebase no longer contains.
Companion Script
core/scripts/verify-learning-staleness.py— the canonical scanner. Modes:- Default (no flag) — JSON to stdout, exit 0 if clean / exit 1 if stale.
--text— human summary on stdout, JSON to stderr.--skill-md <path>— override the target file (default: framework path).
Procedure
-
Bash: py -3 core/scripts/verify-learning-staleness.py --text— run the scanner. Output lists each stale finding as[L<lane>] line N: <stale_ref>with detail explaining why the target couldn't be resolved. -
For each finding, decide the disposition:
- Relocated — the underlying contract still exists, but moved to a different file/phase/script. Update the Check: text to reference the new location.
- Retired — the contract was removed entirely. Delete the Check: line.
- Future-tense — the check was added speculatively for upcoming work. Leave alone if work is genuinely imminent, otherwise convert to a comment or delete.
-
After applying fixes, re-run the scanner —
stale_countshould ratchet down (or stay constant only with documented future-tense exemptions).
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 · 133 lines · 195 tokens per session scan A a762f5c31772
verify-learning-staleness is a skill published in the GitHub repository zkysar1/Claude-Mind (5 stars, last pushed 2d ago), licensed MIT. It adds 195 tokens to every session and 1,614 once invoked, about $0.0010 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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