Pallium
CLAUDE.md2 starsChanged 12 days agoMITView on GitHub
Pallium — local messaging and searchable session history for coding agents. Python 3.12+, FastAPI, SQLite. Follow tools/execution-loop/SKILL.md for any non-trivial change (3+ steps, behavior risk, refactor). Always read before any work: Read on demand: Read when needed, not upfront: Never restart the Windows service through pallium service restart, Task Scheduler, or the launcher directly. After using the wrapper, verify /health, /status, and /debug/queue/health on port 19836.
- Reads credentials
In this repository4 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agent-workflowSkill
- agent-workflowSkill
- minimap-roadmapSkill
The file
We split this CLAUDE.md into the rules it gives an agent
4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 9
Follow tools/execution-loop/SKILL.md for any non-trivial change (3+ steps, behavior risk, refactor).
- Distinctive: line 13
Always read before any work:
- Distinctive: line 26
Read when needed, not upfront:
- Very specific: line 69
Never restart the Windows service through pallium service restart, Task Scheduler, or the launcher directly.
≈ “Common” is an estimate: at least 1 in 5 of the 26,980 CLAUDE.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
CLAUDE.md
Project
Pallium — local messaging and searchable session history for coding agents. Python 3.12+, FastAPI, SQLite.
Execution Discipline
Follow tools/execution-loop/SKILL.md for any non-trivial change (3+ steps, behavior risk, refactor).
Required Reading
Always read before any work:
AGENTS.md— repo-level non-negotiables and delegation rulestools/execution-loop/SKILL.md— execution workflow
Read on demand:
tools/pallium-architect-review/SKILL.md— when reviewing, shaping, or coordinating workagent-policy.yaml+AGENTS.md"agent-redline" section — when classifying a change before editing (red/blue/gray + watch); seedocs/agent/for per-checkpoint guidanceminimap-roadmapskill — when touchingroadmap/files or picking up, resuming, investigating, testing, substantively reviewing, or completing assigned roadmap work; invoke via the Skill tool, lives at.claude/skills/minimap-roadmap/
Context Sources
Read when needed, not upfront:
docs/context/architecture.md— when checking implementation details or boundary questionsdocs/context/decisions.md— when verifying a specific accepted decision or rationaledocs/context/state.md— when checking current test/pipeline statedocs/context/lessons.md— when debugging a problem that might have a known solutiondocs/context/validation.md— read before proposing any new eval — has an Eval Toolbox table mapping intent to existing tool. The repo has 80+ eval scripts and most "would change X have helped" questions are already covered byevals/anchor_probe/thread_replay.py,evals/live_value_scenarios/, orevals/validation_runner.py.roadmap/board.md— when doing roadmap or prioritization work
Commands
# Restart the installed Windows service (canonical; removes the full stale process tree)
powershell -ExecutionPolicy Bypass -File scripts/restart-service.ps1
# Run tests
python -m pytest tests/ -x -q
# Run specific test slice
python -m pytest tests/test_visibility_scope.py -x -q
# Clean all runtime data (DB + vector index) for fresh start
bash scripts/clean-data.sh
# Start server
python -m app.run serve --host 127.0.0.1 --port 8000
# Run agent simulation harness
python -m app.agent_simulation
# Run exploratory QA invariant runner (seed scenarios)
python -m evals.generated_exploratory.invariant_runner
# Run exploratory QA with parallel workers and LLM cache
python -m evals.generated_exploratory.invariant_runner --workers 4 --cache-dir .local/llm-cache
# Run fact consolidation retrieval quality eval
python -m evals.fact_consolidation_eval
# Generate exploratory QA scenarios from taxonomy
python -m evals.generated_exploratory.generator --high-risk-only --count 1 --output evals/generated_exploratory/scenarios/batch.jsonNever restart the Windows service through pallium service restart, Task Scheduler, or the launcher directly. After using the wrapper, verify /health, /status, and /debug/queue/health on port 19836.
Local Config
pallium.local.toml— package/provider structure.env.local— secrets and one-off overrides
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

