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 rjmurillo/ai-agents --skill analysis-provenancegit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/analysis-provenance)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/analysis-provenance"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/analysis-provenance/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/rjmurillo/ai-agents/analysis-provenance"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/analysis-provenance.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 | $0.00048 | $0.02090 |
| Opus 5 | $0.00024 | $0.01045 |
| Sonnet 5 | $0.00010 | $0.00418 |
| Haiku 4.5 | $0.00005 | $0.00209 |
Grade A, and why
analysis-provenance 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 4d 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analysis Provenance
Identify code ownership before modifying validators, linters, or tool configurations.
Triggers
check provenance before modifyingis this file upstream or localwho owns this validatorcheck code ownershipanalyze provenance
Quick Start
# Check provenance of a single file
python3 .claude/skills/analysis-provenance/scripts/check_provenance.py --target .config/markdownlint.json
# Check provenance of a directory
python3 .claude/skills/analysis-provenance/scripts/check_provenance.py --target .config/
# JSON output for automation
python3 .claude/skills/analysis-provenance/scripts/check_provenance.py --target PSScriptAnalyzerSettings.psd1 --format json
Quick Reference
| Category | Meaning | Action |
|---|---|---|
| UPSTREAM | External dependency (npm, pip, NuGet) | Configure, do not modify |
| LOCAL | Project-owned code | Modify as needed |
| VENDOR | Copied/vendored upstream code | Avoid modification, track upstream |
| UNKNOWN | Cannot determine | Investigate before modifying |
When to Use
Use this skill before:
- Modifying any validator or linter behavior
- Changing tool configuration files
- Investigating unexpected validation failures
- Updating analyzer settings
Process
graph TD
A[Target file or directory] --> B["1. Target Resolution<br/>Resolve paths, check exists"]
B --> C["2. Directory Analysis<br/>node_modules, .venv, vendor,<br/>.gitmodules"]
C --> D["3. Package Manifest Analysis<br/>package.json, requirements,<br/>lockfiles"]
D --> E["4. File Header Analysis<br/>First 20 lines: generated<br/>markers, copyright notices"]
E --> F["5. Provenance Determination<br/>Weight signals, return<br/>category + evidence"]
F --> G["Decision:<br/>UPSTREAM - configure only<br/>LOCAL - safe to modify<br/>VENDOR - track upstream<br/>UNKNOWN - investigate first"]
Decision Tree
Need to modify a validator/linter?
│
├─ Run: python3 .claude/skills/analysis-provenance/scripts/check_provenance.py --target <file>
│
├─ Result: UPSTREAM
│ ├─ Do NOT modify the tool/file directly
│ ├─ Adjust local configuration instead
│ ├─ Check for configuration override options
│ └─ If no override exists, document why the rule is suppressed
│
└─ Result: LOCAL
├─ Safe to modify as needed
├─ Follow project coding standards
└─ Test changes before committing
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.
- 4d ago First seen · 291 lines · 48 tokens per session scan A da8d7cfd6e73
analysis-provenance is a skill published in the GitHub repository rjmurillo/ai-agents (45 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 2,090 once invoked, about $0.0002 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 2026-09-03.
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Release workflow for the edloidas/skills collection. Validates git state, analyzes commits since the last tag, recommends a version bump (major/minor/patch), updates Claude and Codex packaging metadata, commits, tags, and pushes.
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Unified adversarial review: v4.3 Strategic Matrix (MTA-004). 7-phase framework: Priors → Rubric → Adversarial Lenses → SWOT/TOWS → MCDA Decision Engine → Blind Spot/Kill Switch → Executive Summary. Absorbs: bias-detector.
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