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 ngreenwall/claude-skills --skill drift-checkgit clone --depth 1 https://github.com/ngreenwall/claude-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/ngreenwall/claude-skills/drift-check)<a href="https://agentmods.dev/skills/ngreenwall/claude-skills/drift-check"><img src="https://agentmods.dev/badge/skills/ngreenwall/claude-skills/drift-check/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/ngreenwall/claude-skills/drift-check"><img src="https://agentmods.dev/badge/skills/ngreenwall/claude-skills/drift-check.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 | $0.00087 | $0.02417 |
| Opus 5 | $0.00044 | $0.01208 |
| Sonnet 5 | $0.00017 | $0.00483 |
| Haiku 4.5 | $0.00009 | $0.00242 |
Grade A, and why
drift-check 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 9d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: Drift Check
A deliberate, manual audit that confirms your global context file (the standing instructions Claude Code loads on every session, ~/.claude/CLAUDE.md) is loaded and actually shaping behavior, and pinpoints where context dropped (top, middle, or bottom of the file). Long sessions and context compaction silently drop rules; this catches it. This skill is Claude Code only, it depends on that global file auto-loading every session, a mechanism other tools don't replicate the same way.
Targets your global context file only; project-level CLAUDE.md is typically short enough that drift is unlikely.
Do NOT pre-read the context file before running the checks. The whole point is to test what is actually loaded in the current context window. Reproduce from memory, then you may verify against the file in step 1.
Setup (one-time, before first use)
This skill needs three canary tokens embedded in your global context file:
- Generate three short, unique, unguessable strings (e.g.
word-word-NNstyle, or any random phrase you won't naturally type in conversation). Use a different token for each position, don't reuse one token in all three spots. - Place one token near the top of the file (alongside your most important always-on rule, so recalling the rule and the token happen together), one in the middle, and one at the bottom. Label each clearly, e.g.
Canary (top): <token>,Mid-file canary: <token>, andCanary (bottom): <token>, and instruct the file's reader not to output the token during normal work, only when running this check. - Note the file path so Check 1 knows what to re-read. Only needed if your global context file lives somewhere other than the standard location (e.g.
~/.claude/CLAUDE.md).
Skip setup if tokens already exist in the file from a previous run.
If you use an always-on convention worth checking every time (e.g. a response-format prefix), name it in your context file, then replace the placeholder examples in this file's own Check 2 "Core rules" list (below) with your actual rules, so scorecards stay comparable across runs.
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.
- 9d ago First seen · 128 lines · 87 tokens per session scan A f27a6b3639f0
drift-check is a skill published in the GitHub repository ngreenwall/claude-skills (2 stars, last pushed 10d ago), licensed MIT. It adds 87 tokens to every session and 2,417 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 2026-08-31.
Other skills, from other repositories
init-workspace-documentation
Skill "init-workspace-documentation" from griddynamics/rosetta, covering agent memory.md, agent memory, preventive rules, what worked and what failed.
ijfw-memory-audit
Audit and clean project memory files. Trigger: 'memory audit', 'clean memory', 'memory health', /memory-audit.
ijfw-handoff
Session handoff generation and loading. Trigger: session end, context full, /handoff.
ijfw-recall
Surface relevant project memory at session start or on demand. Trigger: session start, 'recall', 'remember', 'what do you know', 'context', /recall.
ijfw-summarize
Generate optimized project context from codebase scan. Trigger: new project, no CLAUDE.md, /ijfw-summarize.
repo-context-ledger
Record every behavior-changing feature addition, fix, and adjustment as durable, evidence-based repository knowledge, then use that ledger to continue accurately across AI windows, tools, Git collaboration, and pull requests. Use the deterministic runtime to route bounded context, isolate private drafts, publish…