Borrowing it
Nothing to install: this file belongs to mmnto-ai/totem. 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/mmnto-ai/totem/main/.claude/skills/preflight/SKILL.mdgit clone --depth 1 https://github.com/mmnto-ai/totemWrote 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/mmnto-ai/totem/preflight)<a href="https://agentmods.dev/skills/mmnto-ai/totem/preflight"><img src="https://agentmods.dev/badge/skills/mmnto-ai/totem/preflight/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/mmnto-ai/totem/preflight"><img src="https://agentmods.dev/badge/skills/mmnto-ai/totem/preflight.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 65 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00026 | $0.01596 |
| Opus 5 | $0.00013 | $0.00798 |
| Sonnet 5 | $0.00005 | $0.00319 |
| Haiku 4.5 | $0.00003 | $0.00160 |
Grade A, and why
preflight 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Before starting work on issue $ARGUMENTS, execute these phases in order. Do NOT write code until all gates are cleared.
Phase 1 — Mechanical context gathering
- Run
pnpm exec totem spec $ARGUMENTSto generate the implementation spec from the issue body. Output lands in.totem/specs/<issue>.md. - Call
mcp__totem-dev__search_knowledgewith a query describing the changes you're about to make. If the issue touches a specific system (hooks, orchestrator, compiler, extract pipeline, store layer, MCP server), ALSO querymcp__totem-strategy__search_knowledgefor relevant ADRs and proposals. - Summarize in 3-5 bullets: what the spec says, which lessons are relevant, and any constraints or traps surfaced by the knowledge queries.
If totem spec fails, report the error and stop — do not proceed to Phase 2.
Phase 2 — Scope triage (decide if Phase 3 is needed)
Skip Phase 3 if this is a tactical change. Tactical means ALL of:
- Pure bug fix, wording change, or existing-test tightening
- No new types or required fields on existing types
- No new state containers (maps, sets, module-level variables, singletons)
- No new failure modes or classes of error
- No new cross-cutting concerns (logging, telemetry, caching, auth)
- Touches ≤3 files in a single architectural layer
Draft the design doc (Phase 3) if ANY of:
- Adds a new type, interface, or required field
- Adds a new state container or changes state ownership/lifecycle
- Introduces a new failure mode or changes existing error surface (hard error → warning, sync → async, blocking → non-blocking)
- Crosses architectural boundaries (core → cli, cli → mcp, etc.)
- Touches >3 files OR introduces a new cross-cutting concern
- Documents a new config field, new public API method, or new agent-facing tool surface (regardless of whether the doc is in the same PR as the implementation, and regardless of whether the implementation is yours or someone else's)
When in doubt, draft the doc. The cost of 10 minutes of design writing is always lower than the cost of a multi-round bot review cycle.
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 · 163 lines · 26 tokens per session scan A 4621f694e963
preflight is a skill published in the GitHub repository mmnto-ai/totem (17 stars, last pushed today), licensed Apache-2.0. It adds 26 tokens to every session and 1,596 once invoked, about $0.0001 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.
Other skills, from other repositories
cn-check
Install and run the Continue CLI (cn) to execute AI agent checks on local code changes. Use when asked to "run checks", "lint with AI", "review my changes with cn", or set up Continue CI locally.
printing-press-output-review
Internal sub-skill: agentic review of a printed CLI's sampled command output for plausibility issues that rule-based checks can't encode (substring-match relevance, format bugs, silent source drops, ranking failures). Invoked via the Skill tool by the main printing-press skill at Phase 4.85 and printing-press-polish…
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
review
Validate plans, execution, or PRs against wish criteria — returns SHIP / FIX-FIRST / BLOCKED with severity-tagged gaps.
review
Adversarial fresh-context review of an increment before it ships. Every finding cites path:line and is re-verified. Use when saying "review", "grill this", or "critique the implementation".
genie-orca-review
Independent, read-only review of a group, a wish, or a PR on Orca — SHIP / FIX-FIRST / BLOCKED with severity-tagged findings. Council and retro are this skill with a different input.