agenda-intelligence-md 0.5.2

A drop-in markdown cognition layer for AI agents that need to analyze public agenda

Agenda Intelligence MD

A drop-in markdown cognition layer that turns news scanning into decision-ready analysis.

Bottom line: agents using this protocol move from “monitor developments” to “watch these 3 indicators; if X happens, decision Y becomes urgent.”

---

Who it is for

• Policy analysts & think‑tanks – structured monitoring of regulatory and geopolitical signals.
• Sanctions & compliance teams – evidence‑backed briefs that satisfy audit requirements.
• Market‑risk analysts – concise impact snapshots for faster investment decisions.
• Founders & operators – external chaos turned into clear strategic inputs.
• AI agents – any LLM‑based system that needs to analyze public agenda without drifting into generic summaries.

---

Quick install

From PyPI

pip install agenda-intelligence-md

From GitHub Release (v0.5.2)

pip install https://github.com/vassiliylakhonin/agenda-intelligence-md/releases/download/v0.5.2/agenda_intelligence_md-0.5.2-py3-none-any.whl

Editable install from source

git clone https://github.com/vassiliylakhonin/agenda-intelligence-md
cd agenda-intelligence-md
pip install -e .

---

CLI happy path (aha‑moment in 5‑10 min)

The start command is the primary onboarding entry‑point. It prints a trimmed source plan, a brief template, and next commands.

# 1. Onboard with a source category
agenda-intelligence start technology-ai

# Output: trimmed source plan + brief template + next commands

# 2. Validate a bundled example brief
agenda-intelligence validate-brief examples/agenda-brief.json

# 3. Score a before/after markdown example (quality check)
agenda-intelligence score examples/before-after/eu-ai-act.md

Scorer status: the score command is currently a before/after evaluation harness. It works with markdown examples that contain ## Before: generic agent output and ## After: with Agenda-Intelligence.md, such as examples/before-after/*.md; it does not score arbitrary brief.json files yet.

Demo output (what you see after start):

=== Trimmed source plan ===
{
  "must_check": ["tech‑release", "policy‑update", "market‑data"],
  "watch_indicators": ["regulation draft", "enforcement action"]
}

=== Brief template (fill in) ===
{
  "bottom_line": "<summary>",
  "signal_classification": "<signal>",
  "what_changed": "<what changed>",
  "main_uncertainty": "<main uncertainty>",
  "watch_next": ["<indicator 1>", "<indicator 2>"]
}

---

Protocol · Source Policy · Schemas

Layer	Purpose	Status
Markdown protocol (Agenda-Intelligence.md)	Core reasoning workflow (signal classification, watch‑next, etc.)	✅ Stable
Source Acquisition Layer	Tells the agent which source types to check before making claims (sanctions, regulation, elections, conflict, etc.)	✅ Stable
JSON schemas	Validate briefs, evidence packs, memory cards	✅ Stable
AnalysisBank	Memory layer that stores reusable reasoning patterns from good/bad outputs	✅ Stable
Regional & Sector lenses	Central Asia & Caspian, Middle East, EU; sanctions, export controls	✅ Stable

---

Stable today vs Experimental

✅ Stable today

• Markdown protocol (Agenda-Intelligence.md) – core reasoning workflow.
• JSON schemas – validation for briefs, evidence packs, memory cards.
• CLI validation – validate-brief, validate-evidence, validate-manifest.
• Source plans – source-plan, list-source-packs, source-types.
• Guided start – start command prints trimmed plan + brief template.
• Evaluation toolkit – evals/rubric.md, evals/llm_judge_prompt.txt, evals/human_checklist.md, evals/cases/*.json.

🧪 Experimental / Planned

• MCP integration – the primary adoption channel, but still a sketch (docs/integrations/mcp.md). Full implementation is on the roadmap.
• Fetch command – stub in CLI, full evidence‑pack retrieval not implemented.
• Scorer / eval – score command relies on eval_before_after.py (only in editable installs).
• Generate‑brief – not yet exposed; use start + manual template fill.

Note: The project is young. Stable parts are ready for production use; experimental bits are usable for testing but may change.

---

Examples

• Source‑backed briefs (with evidence mode & source plans):
  examples/source-backed/eu-ai-act.md, sanctions-routing.md, red-sea-shipping.md
• Classic examples: examples/hormuz_strait_brief.md, eu-brief.md, central-asia-caspian-brief.md
• Before/after: examples/before-after/ – shows the delta when the protocol is applied.

---

Documentation

Resource	Link
Quickstart	docs/quickstart.md
End‑to‑end tutorial	docs/tutorial.md
Evaluation assets	evals/ – rubric, LLM judge prompt, human checklist, sample cases
Use‑cases	docs/use-cases/ – policy monitoring, sanctions compliance, market risk, founder context
Integrations	docs/integrations/ – Claude Code, OpenAI Codex, Cursor, MCP
Roadmap	ROADMAP.md
Changelog	CHANGELOG.md

---

Repository structure

agenda-intelligence-md/
├─ src/agenda_intelligence/   # Python package
├─ schemas/                   # JSON schemas
├─ examples/                  # sample briefs, evidence packs, source‑backed examples
├─ analysis-bank/             # memory cards (failures & successes)
├─ evals/                     # evaluation rubric, LLM judge, human checklist, cases
├─ docs/                      # guides, tutorials, use‑cases, integrations
├─ skills/agenda-intelligence/ # OpenClaw skill wrapper
└─ tests/                     # pytest suite

---

Roadmap (high‑level)

• v0.6 – Full MCP server exposing all tools via HTTP/WebSocket.
• v0.7 – Complete get_protocol, list_lenses, get_lens, source_plan, score_output in MCP.
• v0.8 – Automated CI quality gate using the evaluation toolkit.
• v1.0 – Stable API, broader adoption‑channel support, production‑grade MCP.

---

Contributing

Pull requests are welcome! Please:

1. Open an issue to discuss changes.
2. Create a feature branch (feat/..., fix/...).
3. Run pytest and ensure all tests pass.
4. Update docs if behavior changes.

---

License

MIT – free for any use.

---

Why this exists

Most agent‑written news analysis is a polished recap that doesn’t change any decision. This project gives agents a stricter workflow so their output actually helps someone decide, hedge, or act.