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 agentmods add skills/swingerman/engineer/consistency-checknpx skills add swingerman/engineer --skill consistency-checkgit clone --depth 1 https://github.com/swingerman/engineerWhat 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 | $0.00053 | $0.01299 |
| Opus 5 | $0.00026 | $0.00649 |
| Sonnet 5 | $0.00011 | $0.00260 |
| Haiku 4.5 | $0.00005 | $0.00130 |
Grade A, and why
consistency-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 2d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
consistency-check
Cross-artifact validation for DAE — the methodology's take on Speckit's /analyze. Read-only: reports inconsistencies and suggests fixes; never mutates. Any agent may run it (mechanical validation, not judgment-heavy verification). checkpoint: null.
When to use
Before major pipeline transitions and as a CI gate.
- Feature scope —
/engineer.consistency-check <slug>— one feature's artifacts - Project scope —
/engineer.consistency-check --project— project-wide invariants
Not for: fixing inconsistencies (run the suggested fix skill); within-one-artifact ambiguity (clarify); code risk (crap-analyzer).
Workflow
- Resolve + load — resolve the methodology root + manifest via
${CLAUDE_PLUGIN_ROOT}/scripts/dae_resolve.py(seereferences/resolving.md). Feature scope: load every artifact infeatures/NNN-<slug>/+CHARTER.md. Project scope: loadCHARTER.md+ everyfeature.mdfrontmatter. For identifier lookups (does symbol X actually exist in the code?), prefer LSP find-definitions / workspace-symbols when an LSP MCP capability is available; fall back to grep + Read otherwise. See${CLAUDE_PLUGIN_ROOT}/references/code-lookup.md. 1.5. Run the mechanical checks first —${CLAUDE_PLUGIN_ROOT}/scripts/dae_ontology.py <feature-dir>(or--project). It executes every constraint that is a set operation over structured data — enumerations, functional properties, parent/child inverses, cycle detection, AC↔scenario coverage closure, Principle 7 disjointness — deterministically. Take its findings as given; do not re-derive them by reading the artifacts. They are tagged in the tables below as (ontology). See${CLAUDE_PLUGIN_ROOT}/references/ontology.md. - Run the judgment checks (below) — the rows the script cannot decide.
- Report — errors first, then warnings; each with location + a suggested fix (which skill to run). Do not apply fixes.
- Handoff — emit a summary.
Feature-scope checks
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.
- 2d ago First seen · 73 lines · 53 tokens per session scan A f46e4f5bf653
consistency-check is a skill published in the GitHub repository swingerman/engineer (144 stars, last pushed 6d ago), licensed MIT. It adds 53 tokens to every session and 1,299 once invoked, about $0.0003 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.
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