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 adamlinscott/claude-skills --skill regroundgit clone --depth 1 https://github.com/adamlinscott/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/adamlinscott/claude-skills/reground)<a href="https://agentmods.dev/skills/adamlinscott/claude-skills/reground"><img src="https://agentmods.dev/badge/skills/adamlinscott/claude-skills/reground/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/adamlinscott/claude-skills/reground"><img src="https://agentmods.dev/badge/skills/adamlinscott/claude-skills/reground.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 | $0.00095 | $0.00981 |
| Opus 5 | $0.00048 | $0.00491 |
| Sonnet 5 | $0.00019 | $0.00196 |
| Haiku 4.5 | $0.00010 | $0.00098 |
Grade A, and why
reground 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reground
The current session has drifted: the recent work rests on assumptions the codebase has not been shown to support, invents systems that may not exist, or builds past what the task needs. This skill is a hard reset to ground truth. It does not summarize or compact — it discards the speculative thread and rebuilds a small, evidence-backed footing for the next step. Run the protocol in order. Do not skip ahead to step 5.
1. Halt
Stop immediately. Make no further Edit, Write, or other tool calls that advance the
work until the report in step 5 is produced and the user approves a next step.
Before reporting, make sure the working tree is in a coherent state — never leave a file half-written or a multi-file change partly applied. If a change is mid-flight, the only edits permitted here are the minimal ones that restore a self-consistent, non-broken state, or a clean revert of it; make no new feature progress either way. The aim is solid footing to assess from, not a frozen half-edit.
2. Name the drift
State plainly, in one or two sentences, the specific unverified assumption(s) the recent work depended on — the equivalent of "the last answer assumed X, but the repo shows no evidence for X." Be concrete: name the assumed file, function, endpoint, schema, or behavior. If you cannot name what was assumed, that itself is the finding — say so.
3. Set the anchor
Decide the focus file set — the minimum slice of the repo the next step actually depends on:
- If the user passed paths or class/function/object names (as arguments or in their message), use exactly those.
- Otherwise, self-derive them from the stated task: list the files/paths you intend to read and why each one matters, then read them. Keep the set small and task-scoped; do not sweep the whole repo. If the right anchor is genuinely unclear, ask the user for the paths rather than guessing.
4. Read the anchor, nothing else
Read those files now. Treat only what is on disk as true. Do not fill gaps with memory, convention, or inference about code you have not opened. If something the task seems to need is absent, that is an "unknown," not a thing to assume into existence.
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 · 74 lines · 95 tokens per session scan A d4b179d7add0
reground is a skill published in the GitHub repository adamlinscott/claude-skills (3 stars, last pushed 9d ago), licensed MIT. It adds 95 tokens to every session and 981 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 2026-08-31.
Other skills, from other repositories
hns-lsel-curator
Local Self-Evolution Loop (LSEL) curator — the CLUSTER + drain engine for the GOOS-local PROPOSE→APPLY seam closure (SPEC-LSEL-LOCAL-EVOLUTION-001). Companion-offset drain of .moai/lessons-inbox.jsonl with a drain-side severity filter that drops the 65% Bash-timeout/sandbox noise, eventkey clustering with a frequency…
moai-workflow-ddd
Domain-Driven Development workflow specialist using ANALYZE-PRESERVE-IMPROVE cycle for behavior-preserving code transformation. Use when refactoring legacy code or reducing technical debt safely.
moai-workflow-loop
Ralph Engine - Automated feedback loop with LSP diagnostics and AST-grep integration for continuous code quality improvement. Use when implementing error-driven development, automated fixing, or continuous quality validation workflows.
moai-workflow-testing
Use when writing tests, measuring coverage, or running characterization, performance, or PR-review QA. Comprehensive specialist combining DDD testing, characterization tests, performance profiling, and TRUST 5 quality-assurance validation.
dev-doctor
Run a development-focused health check on the AIWG repository structure.
config-validator
Validate AIWG configuration files and project setup for correctness and completeness.