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When you close a deal, ship a feature, or lose a client — how did it happen? Which decisions, in what order, against which context, on which hypotheses? Today's AI agents can't answer that. They follow skills and instructions perfectly, but they don't accumulate grounded knowledge about how outcomes actually arrived.
Outsource your understanding. Capture every human-AI decision as a step in a trajectory; grade trajectories retroactively when outcomes land (deal closed, sprint shipped). Hooks + MCP server for Claude Code, Qdrant + FastEmbed local-first.
OpenExp captures every human-AI decision as a step in a trajectory, links those steps into coherent journeys, and grades each journey retroactively when reality returns its verdict — a deal closes, a sprint ships, a payment lands. The result is a continuously growing labeled dataset of decisions tied to outcomes, ready to train domain-specific intuition.
We do not hand-craft features at step level (tone: urgent, signal: positive, hypothesis: probable). Pre-labeling injects the labeler's biases and corrupts the eventual training signal. Same hygiene as credit scoring: collect rich features per applicant, label only the terminal outcome (paid / didn't), let the model learn what predicts repayment from data alone.
Only terminal outcomes get labels:
outcome — closed_won / closed_lost / failed / abandonedgrade — 0.0 to 1.0, school-styleSteps are stored raw. Authors annotate their own intent, hypotheses, and decisions ("I believed X at this point", "I chose Y because Z"). They do not label the signal quality of individual events — that's what the eventual model learns.
Casual analogy: kids in school don't get annotations on every homework problem. They turn in work, get a grade at the end of the term, and develop intuition over hundreds of grades.
git clone https://github.com/anthroos/openexp.git
cd openexp
./setup.sh
That installs the four hooks into Claude Code, makes sure Qdrant is up, and registers the MCP server.
If something is already serving Qdrant on localhost:6333, the script uses it and leaves it alone. Otherwise it starts Qdrant in Docker for you.
Prerequisites: Python 3.11+, jq, and Qdrant — either Docker (the script handles the container) or a native Qdrant binary you run yourself.
No API key required for core functionality. Embeddings run locally via FastEmbed. An Anthropic API key is optional and only powers the two-prompt pipeline (anonymize + extract experience) when you publish.
Four hooks run automatically inside Claude Code:
Retrieval ranks via semantic similarity + BM25 + recency. No magic numbers. No Q-value scoring component.
When you decide to publish an experience — turn a real, terminal trajectory into a shareable artifact — two prompts do the work:
prompts/anonymize.md — takes raw trajectory data (transcripts, emails, decisions) and produces an anonymized YAML trajectory. PII is replaced by category tokens (<counterparty_cto>, <regulated_industry>, <value:10k-100k>, <local_currency>, day_+5) while structural features are preserved. The prompt enforces a reverse-identification rule: tokens narrow enough to identify a counterparty in jurisdiction must be generalized one level up before publication.
prompts/extract_experience.md — reads the anonymized trajectory plus the terminal outcome label and produces a facts-only meta.yaml (id, outcome label, duration, step count, category tokens, license). It deliberately refuses to write applies_when, searchable_summary, or a grade reason — those are interpretations and belong to the reader's Claude at use time, not to the publisher at publish time.
You run both prompts inside your own Claude Code, against your own Qdrant. Nothing is sent to a central server.
A published experience is four files in a UUID-named directory (schema v3, 2026-04-27):
experiences/<uuid>/
├── meta.yaml # facts only: id, outcome label, duration, category tokens, license
├── trajectory.anonymized.yaml # raw ordered timeline of N steps, anonymized
├── README.md # human-readable face for the marketplace
└── SKILL.md # Claude entry point — read first when skill is invoked
meta.yaml shape (abridged from seed d49e0997):
pack:
id: d49e0997-8455-4d3c-90ca-d6cf54d0f662
author: ivan-pasichnyk
license: MIT
schema_version: 3
outcome:
label: closed_won # fact, not interpretation
closed_at: day_+57
duration_days: 57
step_count: 26
category_tokens: # what appears in the trajectory
- <counterparty_cto>
- <counterparty_pm>
- <regulated_industry>
- <e_signing_platform_local>
# ...
No applies_when, no searchable_summary, no grade_reason. Earlier schemas (v2) baked the publisher's read of the timeline into the artifact — one Claude's interpretation, frozen. Schema v3 inverts that: the pack ships raw, and the reader's Claude derives match on the fly against the reader's actual situation. Different readers, different contexts, different inferences from the same trajectory. See CHANGELOG.md for the full v2 → v3 transition rationale.
A published experience is a namespaced Claude Code skill:
openexp:<author-handle>:<experience-slug>
Drop the pack into ~/.claude/skills/openexp:<author>:<slug>/ (rename the directory to the skill-namespaced form on copy). Claude Code auto-discovers it on the next session.
# Install the seed pack as a skill
cp -r ~/openexp/experiences/d49e0997 \
~/.claude/skills/openexp:ivan-pasichnyk:inbound-acquisition-with-free-pilot
Two layers of identity:
SKILL.md inside the pack is the entry point — it tells the user's Claude when to invoke, how to use the trajectory, and what not to do (no fabrication, no de-anonymization, attribution required).
See docs/skill-architecture.md for the full naming convention, install flow, and design rationale.
The experiences/ directory in this repo is the seed of an eventual marketplace. Published packs are listed in CATALOG.md. The publication format works; seeds will accumulate. A directory of installable experiences is the eventual surface, not a built product today.
Five focused tools (hippocampus model — write everything, retrieve selectively):
Pack-grounded predictions are how the system learns whether a published experience pack actually moves real-world outcomes. Without prediction/outcome pairs, pack value cannot be measured against any baseline, and any future experiment (cross-pack voting, embedding retrieval, new packs from new authors) is unfalsifiable.
Trigger criterion is sharp. Logging fires only when the assistant cites a pack's specific relative_day as the reason for an action recommendation. No day-citation → no log. Description of a situation without a recommendation → no log. This keeps the dataset honest and the cost low.
log_prediction (new path, schema_version 2)
log_outcome (new path, schema_version 2)
What's deliberately NOT in the schema: confidence (Claude-side confidence is uncalibrated until ≥30 outcome datapoints), alternative_action_if_no_pack and predicted_outcome_alternative (the same Claude that writes the prediction would invent the counterfactual, biased toward "the pack helped" — real ablation needs a pack-blind run, separate track).
Backward compatibility. The legacy schema (prediction, confidence, strategic_value, memory_ids_used) is still accepted by both tools and recorded as data. As of 2026-07-03 nothing updates Q-values — the Q engine is fully deleted. New-path entries are marked schema_version: 2 in the JSONL row.
openexp search -q "stalled enterprise procurement" -n 5
openexp ingest # ingest pending transcripts into Qdrant
openexp stats # collection + prediction stats
Environment variables (.env):
Pilot. Architecture freeze landed 2026-04-26. First experience seed published: experiences/d49e0997/ — a 57-day inbound acquisition that closed at grade 1.0 (author's own assessment), anonymized to category tokens.
Honest about what isn't done:
author_intent, author_hypothesis, author_decision) are a likely near-term addition.See docs/redesign-2026-04-26.md for the full architecture freeze and docs/claude-design-brief.md for the v2 product framing.
This project is in early stages. See CONTRIBUTING.md for setup and workflow.
The most useful contribution right now is publishing a real experience. Take one of your own closed trajectories, run it through prompts/anonymize.md and prompts/extract_experience.md, and open a PR adding a new directory under experiences/.
MIT © Ivan Pasichnyk
Outsource your understanding. Capture every human-AI decision as a step in a trajectory; grade trajectories retroactively when outcomes land (deal closed, sprint shipped). Hooks + MCP server for Claud
개요
Outsource your understanding. Capture every human-AI decision as a step in a trajectory; grade trajectories retroactively when outcomes land (deal closed, sprint shipped). Hooks + MCP server for Claude Code, Qdrant + FastEmbed local-first.
도구
아직 도구 목록이 없습니다. 원격 서버는 첫 상태 확인 후에 도구 목록이 생깁니다.
FAQ
이 페이지에서 Cursor, Claude Desktop, Claude Code 또는 VS Code용 stdio 설정을 복사합니다. PlugMCP 프록시가 아니라 게시자의 설치 방식을 사용합니다.
이 레코드에는 필수 환경 변수가 등록되어 있지 않습니다.
Openexp은(는) stdio를 통해 로컬에서 실행됩니다.
마지막 확인: 2026-10-27. Outsource your understanding. Capture every human-AI decision as a step in a trajectory; grade trajectories retroactively when outcomes land (deal closed, sprint shipped). Hooks + MCP server for Claude Code, Qdrant + FastEmbed local-first.