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 shippy/personal-intelligence-kit --skill cross-source-queriesgit clone --depth 1 https://github.com/shippy/personal-intelligence-kitWrote 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/shippy/personal-intelligence-kit/cross-source-queries)<a href="https://agentmods.dev/skills/shippy/personal-intelligence-kit/cross-source-queries"><img src="https://agentmods.dev/badge/skills/shippy/personal-intelligence-kit/cross-source-queries/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/shippy/personal-intelligence-kit/cross-source-queries"><img src="https://agentmods.dev/badge/skills/shippy/personal-intelligence-kit/cross-source-queries.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 | $0.00051 | $0.00983 |
| Opus 5.5 | $0.00020 | $0.00393 |
| Sonnet 5.5 | $0.00010 | $0.00197 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
cross-source-queries 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Source Queries
Analysis tools that detect patterns across multiple data sources. Each analysis reads vault.toml to discover which sources are enabled and skips missing ones gracefully.
The intention-reality analysis uses an LLM (Claude Sonnet or GPT) for semantic goal assessment against weekly reflections when ANTHROPIC_API_KEY (or OPENAI_API_KEY) is set, and falls back to regex heuristics otherwise. The other two analyses use regex-based heuristics and have EXTENSION POINT comments marking where to add LLM calls.
Invocation
cd .claude/skills/cross-source-queries
uv run generate_report.py # Run all three analyses
uv run intention_reality_gaps.py # Just intention-reality
uv run commitment_accountability.py # Just commitments
uv run serendipity_convergence.py # Just convergence
Or via Claude Code: batch: cross-source-queries
Available Analyses
1. Intention ↔ Reality Gaps (intention_reality_gaps.py)
Compare stated yearly goals vs actual behavior, primarily via weekly reflections.
What it does:
- Finds goal files in notes vault (e.g., "2026 Goals.md") and parses them structurally (sections, sub-goals, checkbox/strikethrough state)
- LLM path (when
ANTHROPIC_API_KEYorOPENAI_API_KEYis set): feeds the goals, all weekly reflections inoutput/reflections/, and supplementary keyword-match signals into a single LLM call that classifies each goal asactive/stale/neglected/completed/postponed - Heuristic fallback (no API key): keyword-matches each goal against notes, email, and tasks — noisy but runs offline
- Appends recent intentions from
journal.db
Output: output/reports/intention-reality-YYYY-MM-DD.md
Data sources: notes vault, output/reflections/, journal.db, tasks.db, email (notmuch)
2. Commitment Accountability (commitment_accountability.py)
Track commitments made in email and check follow-through.
What it does:
- Scans sent emails for commitment phrases (regex patterns)
- Checks for follow-up emails to same recipient
- Flags commitments without follow-through
What ships with it
4 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.
- 1mo ago First seen · 89 lines · 51 tokens per session scan A 6e94d0f76147
cross-source-queries is a skill published in the GitHub repository shippy/personal-intelligence-kit (14 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 983 once invoked, about $0.0002 per session on Opus 5.5. 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.
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