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/bjorn-ingmanson/thefroject-pluginsWrote 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/bjorn-ingmanson/thefroject-plugins/intelligence)<a href="https://agentmods.dev/commands/bjorn-ingmanson/thefroject-plugins/intelligence"><img src="https://agentmods.dev/badge/commands/bjorn-ingmanson/thefroject-plugins/intelligence/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/bjorn-ingmanson/thefroject-plugins/intelligence"><img src="https://agentmods.dev/badge/commands/bjorn-ingmanson/thefroject-plugins/intelligence.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 | $0.00457 |
| Opus 5.5 | $0.00000 | $0.00183 |
| Sonnet 5.5 | $0.00000 | $0.00091 |
| Haiku 4.5 | $0.00000 | $0.00046 |
Grade A, and why
intelligence 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 2mo 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/intelligence — GTM Learnings Log
Capture and retrieve GTM intelligence so wins, losses, and objections become reusable knowledge instead of tribal memory.
Modes
/intelligence add [what you learned]— log a new insight/intelligence find [topic]— search existing entries/intelligence review— summarize what has been captured in the last 30 days
When you add
Walk the user through a short capture:
- Source — campaign, call, experiment, feedback, manual hunch.
- Insight — one-sentence takeaway. Concrete, not vague. "Subject lines with the prospect's city outperform generic geo references by ~20% in enterprise" beats "personalization works."
- Segment — ICP, role, industry, deal stage. Which slice this applies to.
- Confidence —
hypothesis(one data point),validated(repeatable in one channel),proven(holds across channels or over time). - Evidence — link or note for where the insight came from.
Append to context/intelligence.md under the right section (Wins / Losses / Objections / Messaging / Channels). Create the file with these sections if it does not exist. One bullet per learning, tagged with segment and confidence.
When you find
Search context/intelligence.md and related outputs/ files for matching entries. Return:
- Top 3 most relevant entries with their confidence level.
- Any conflicting entries (one says X works, another says it does not) flagged for review.
- A suggested next action ("you have a validated learning here — apply it to [current work]").
When you review
Summarize additions from the last 30 days: what was learned, which segments were covered, which are underrepresented. Flag stale hypotheses (older than 60 days, never validated) for retirement.
Usage: /intelligence [add|find|review] [optional text]
Examples:
/intelligence add short subject lines beat long ones in cold outreach to ops leaders/intelligence find pricing objections for mid-market/intelligence review
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.
- 2mo ago First seen · 39 lines · 0 tokens per session scan A 263731b74150
intelligence is a command published in the GitHub repository bjorn-ingmanson/thefroject-plugins (1 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 457 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.
intelligence compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| intelligencebjorn-ingmanson/thefroject-plugins | Command | 1 | 4mo ago | MIT | A | 0 | Claude Code |
| checklistgithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| analyzegithub/spec-kit | Command | 148k | yesterday | MIT | A | 26 | Claude Code |
| convergegithub/spec-kit | Command | 148k | yesterday | MIT | A | 35 | Claude Code |
| implementgithub/spec-kit | Command | 148k | yesterday | MIT | A | 14 | Claude Code |