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 Eliyce/paqad-ai --skill cross-module-impact-scannergit clone --depth 1 https://github.com/Eliyce/paqad-aiWrote 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/eliyce/paqad-ai/cross-module-impact-scanner)<a href="https://agentmods.dev/skills/eliyce/paqad-ai/cross-module-impact-scanner"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/cross-module-impact-scanner/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/eliyce/paqad-ai/cross-module-impact-scanner"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/cross-module-impact-scanner.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 | $0.00029 | $0.00963 |
| Opus 5 | $0.00015 | $0.00481 |
| Sonnet 5 | $0.00006 | $0.00193 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
cross-module-impact-scanner 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 12d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What It Does
Reads a proposed implementation outline before any code is written and predicts which other modules will be affected. For every public surface the change touches — API contract, event, schema, configuration, shared utility — classifies the impact on every consuming module and recommends the coordinated changes those consumers will need.
The point is to surface contract breakage in design phase, not after a deploy fails.
Use This When
Use this in the graduated and full lanes whenever the proposed change touches at least one item that crosses module boundaries: a public API, a published or consumed event, a shared database table, a shared configuration value, or a re-exported utility. Skip when the change is purely internal to one module and the integration docs confirm no consumers exist.
Inputs
- Read the proposed solution at
proposed_solution_pathfirst. - Read the module map at
module_map_pathto learn the canonical module slugs. - Read integration docs and API docs for each module the change references; these are the source of truth for consumer relationships.
- Read
references/impact-classification.mdbefore classifying any impact so the severity rubric stays consistent.
Procedure
- Run
scripts/list-modules.shto load canonical module slugs from the module map. - Run
scripts/find-integration-docs.shto enumerate per-module events/contracts/integration docs. - Enumerate every public surface the proposed solution changes (API, event, schema, config, shared utility).
- For each (surface, consumer) pair, classify severity using
assets/severity-rubric.txt(breaking | silent-shift | additive | internal-only). Default tobreakingwhen in doubt; downgrade tosilent-shiftwhen anadditiveclaim has no doc update. - For
breaking/silent-shiftimpacts with no deprecation window, add a Decision Packet entry (category fromassets/severity-rubric.txtexamples). - Format per
assets/output.template.md; validate withscripts/lint-output.sh.
What ships with it
7 files 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.
- 12d ago First seen · 89 lines · 29 tokens per session scan A dd4e45170416
cross-module-impact-scanner is a skill published in the GitHub repository Eliyce/paqad-ai (8 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 963 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 2026-08-31.
Other skills, from other repositories
pr-writing-review
Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer…
session-investigator
Investigate fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool…
auto-go
A command that implements code from a SPEC, a document describing the required behavior and work.
auto-plan
A code-planning skill that examines a codebase and creates a detailed specification, implementation plan, and acceptance criteria. It can organize requirements using EARS, a structured way to describe how software should behave in different situations.
agent-pipeline
Multi-agent pipeline orchestration skill.
adaptive-quality
Per-task execution profile selection based on complexity in Balanced quality mode.