Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Borda/AI-Rignpx agentmods add skills/borda/ai-rig/agent-shimsWrote 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/borda/ai-rig/agent-shims)<a href="https://agentmods.dev/skills/borda/ai-rig/agent-shims"><img src="https://agentmods.dev/badge/skills/borda/ai-rig/agent-shims/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/borda/ai-rig/agent-shims"><img src="https://agentmods.dev/badge/skills/borda/ai-rig/agent-shims.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.01119 |
| Opus 5.5 | $0.00011 | $0.00448 |
| Sonnet 5.5 | $0.00006 | $0.00224 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
agent-shims 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 16d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Before asking, read User Questions.
Agent Shims
After user intervention or repeated invocation, follow Resume And Re-entry: complete selected action's verification and diagnostic instructions below. This manager keeps its non-artifact lifecycle; it doesn't skip its closing checks or replace final explanation with raw JSON.
This is experimental lifecycle tool. Installing authenticated standalone agent TOML does not prove that active collaboration interface can select that custom profile. A task name or child path matching role name is not proof. Use installed shims only when runtime exposes explicit custom-agent selector and observed child metadata plus verifier output prove selection; otherwise use blank-agent role-card injection.
Accept exactly one action: doctor, status, install, or remove. Reject missing, extra, or unknown arguments without writes.
Any follow-up question follows User Questions: put accepted action names or required input format in the permitted native control after showing context; use plain chat only when no permitted native control is suitable. Preserve the manager's exact-digest removal confirmation syntax; never substitute a generic yes/no response for that digest-bound input. The blocked install action still requires no approval prompt.
Locate ../../scripts/manage_role_agents.py relative to this installed SKILL.md. Run it with current Python 3.10+ interpreter and selected action. Never copy manager, resolve it through source checkout, or edit generated agent files directly.
doctor: run read-only live prerequisite check, render its JSON result for person.status: run read-only health and installed-roster summary, render its JSON result for person.install: report stable platform block. Never request approval; no new shim plan or write is allowed.remove: use same exact-digest flow to remove every intact managed shim. Never delete by filename prefix or marker alone. On failure, apply the same per-exit-code recovery guidance listed below fordoctorandstatus— it applies toremove's failure modes too.
Preserve manager's exit contract: 0 success/converged, 2 usage, 3 cancelled, 4 drift/conflict, 5 prerequisite blocked, 6 untrusted state, 7 internal/recovery failure. Never retry mutating action after codes 4, 6, or 7; report evidence, keep files untouched.
For doctor and status, never return only raw JSON or generic safety label:
- Start with plain-English explanation of whether shims are usable and what needs attention. Then give
Healthy,Degraded, orBlocked, followed by first non-pass check and its exact detail; never expose raw diagnostic JSON as whole answer. - List every other non-pass check once, then summarize
state,targets,recovery, namespace candidates. - State
No files changed. - Give narrow safe next step. For package or active-package failures, refresh or reinstall Codex Rig, start fresh session. For executable failures, report selected path, rerun from fresh session with stable Python and Codex selection. For permission, owner, type, or link failures, inspect only named path, verify its metadata before changing anything. For corrupt, inconsistent, modified, or foreign evidence, back it up, never adopt, edit, or delete it automatically. For recognized recovery residue, use
remove, review its authenticated approval digest.
Never recommend recursive chmod, chown, deletion, or link replacement from diagnostic alone. On POSIX systems, a protected agent target may be readable by other users, but it must be owned by current user, have no group/world write or special permission bits; private lifecycle state remains exact mode 0700. Not applicable on Windows, where remove is platform-blocked.
After successful install or removal, tell user to start fresh Codex session. Thin shims intentionally depend on installed plugin cache; uninstalling plugin makes remaining shims unavailable until safely removed or reinstalled.
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.
- 16d ago Changed fe22c2ae1d2b
- 23d ago Changed c49368e80321
- 24d ago Changed be960b45f41e
- 28d ago Changed 13846e345afc
- 29d ago Changed · +2 lines 9f64285f4e4c
- 1mo ago Changed · +4 lines 6e51cc5414db
- 1mo ago First seen · 43 lines · 28 tokens per session scan A 7b4ebeaf345d
agent-shims is a skill published in the GitHub repository Borda/AI-Rig (28 stars, last pushed today), licensed Apache-2.0. It adds 28 tokens to every session and 1,119 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
i18n-parity
MANDATORY for every coding agent and contributor touching localized content — keep all five localization surfaces (dashboard UI keys, wiki page, full mirrored guides, locale-aware formatting, language switchers) in parity across every supported language. Use automatically (without being asked) whenever you add or…
file-headers
MANDATORY for every coding agent (Claude Code, Codex, or any other) on every change-set — every applicable source file the agent creates or updates MUST start with the project's copyright/authorship header (file overview + exact author line). Use automatically whenever writing a new file or editing an existing one; do…
productivity-score
Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cacheread vs input), compaction pressure (baseline tokens), turn velocity (turncount / totalturndurationms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and…
dashboard-status
Quick dashboard health and status overview — checks the Agent Monitor API (port 4820), reports session/agent/event counts from /api/stats, confirms WebSocket connectivity, reads the redacted hook status returned by /api/settings/info, and shows data freshness (last event timestamp). Use to verify the monitoring system…
dag-map
Render the multi-agent orchestration DAG for a session — parent→child subagent edges, tree depth, and fan-out — from the Agent Monitor workflow intelligence API. Cross-checks the orchestration dataset against the raw agent records and session detail. Use when visualizing how a session's agent structure was organized.
regression-watch
Detect quality and efficiency regressions over time using Agent Monitor data — rising error rate (APIError events), falling cache hit rate, growing compaction frequency, and climbing cost-per-session. Splits history into an earlier baseline window and a recent window and reports which metrics are getting worse, by how…