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.
git clone --depth 1 https://github.com/jonase47/ccprWrote 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/commands/jonase47/ccpr/cross-check)<a href="https://agentmods.dev/commands/jonase47/ccpr/cross-check"><img src="https://agentmods.dev/badge/commands/jonase47/ccpr/cross-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/commands/jonase47/ccpr/cross-check"><img src="https://agentmods.dev/badge/commands/jonase47/ccpr/cross-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 | $0.00000 | $0.02268 |
| Opus 5.5 | $0.00000 | $0.00907 |
| Sonnet 5.5 | $0.00000 | $0.00454 |
| Haiku 4.5 | $0.00000 | $0.00227 |
Grade A, and why
cross-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 1mo 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cross-check – Cross-Artifact Consistency Check Across Phases
Reads phase indexes and selected detail files, checks inconsistencies between phases (architecture ↔ implementation, features ↔ stories, threats ↔ mitigations, etc.).
Bridges the gap between intra-phase lints (phase-docs-lint.sh) and gate content evaluation —
finds contradictions no other command surfaces.
Recommendation, not a mandatory step. Gates list /cross-check as an optional pre-flight check.
Argument: $ARGUMENTS = [optional: projectdir]
- Without argument: operates on
$(pwd).
Prerequisites
- Project has at least 2 completed phases (otherwise there is no "cross")
- Phase indexes exist:
docs/discovery/DISCOVERY.md,docs/concept/CONCEPT.md,docs/architecture/ARCHITECTURE.md,docs/planning/PROJECT_PLAN.md, optionallydocs/quality/QA.md - Optional:
docs/CONSTITUTION.md(for R6)
Lead Agent
code-reviewer with system-architekt as consultant for tech-specific findings.
Rule Catalogue v1 (7 Rules)
R1: FEATURES.md ↔ AUTH.md / User-Stories
Every user-facing feature (login, profile, permissions) must have a corresponding flow in AUTH.md.
Sources:
docs/concept/FEATURES.md(feature list, MoSCoW)docs/architecture/AUTH.md(auth flows)docs/planning/BACKLOG.md(user stories as cross-reference)
Check: For each feature tagged as user-facing (heuristic: mentions "login", "account", "profile", "sign-in") → is a matching flow described in AUTH.md?
R2: TECH_STACK.md ↔ DATA_MODEL.md (DB choice consistent)
If TECH_STACK.md names PostgreSQL, DATA_MODEL.md must not use MongoDB syntax (and vice versa).
Sources:
docs/architecture/TECH_STACK.md(DB choice, language, framework)docs/architecture/DATA_MODEL.md(schema, ERD)docs/architecture/API_SPEC.md(endpoints)
Check: Extract DB term from TECH_STACK (regex PostgreSQL|MongoDB|SQLite|SwiftData|MySQL); search DATA_MODEL for syntax markers (CREATE TABLE, NoSQL JSON schema, @Model SwiftData decorators). Report inconsistencies.
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.
- 1mo ago Changed 977753dd5b52
- 1mo ago Changed f2e423d01a79
- 1mo ago First seen · 199 lines · 0 tokens per session scan A fde93002221d
cross-check is a command published in the GitHub repository jonase47/ccpr (1 stars, last pushed 26d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,268 tokens. 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 commands, from other repositories
stats
Show real token usage, delegation rate, and actual savings for the current Hydra session (parses Claude Code session JSONL — no AI estimation).
map
View, rebuild, or query the codebase dependency map.
design-intent
Guided UI/visual development with structured exploration, architecture design, and quality review.
ralph
Ralph autonomous development loop with fresh context per iteration.
debug
Systematic debugging with automated investigation and 4-phase methodology. Default: inline evidence gathering and diagnosis. --deep: spawns systematic-debugger agent for full autonomous debugging. Use for errors, stack traces, test failures, or unexpected behavior.
double-check
Comprehensive verification with automated quality gates, confession gathering, and confidence-based issue filtering. Default: self-review with gates. --deep: multi-agent review via code-reviewer agent. Use before committing, after fixing bugs, or to validate feature completeness.