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Repository Context Layer — a git-native context.md standard for AI agents: consult before planning, write back what was learned

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- - analyze_p2.py
  - consultation_audit_analyze.py
  - consultation_audit_sample.py
  - count_episodes.py
  - power_sim.py
  - power_sim_results.txt
  - reproduce_paper.py
- - consultation-hand-audit-40.csv
  - consultation-hand-audit-40.key.csv
  - CONSULTATION-HUMAN-VERIFICATION.md
  - README.md
- - swebench_lite.parquet
  - swebench_verified.parquet
  - verified_subsystems.json
- - eval_all.py
  - eval_opus_ext.py
  - eval_p2_be.py
  - instances_opus_ext.json
  - instances_paper2.json
  - pairs_frontier.json
  - pairs_tier1.json
  - pairs_tier2.json
  - promote.py
  - README.md
  - resume_matrix.sh
  - resume_opus_ext.sh
  - run_batch.py
  - run_condition.py
  - run_opus_ext.py
  - templates.py
  - templates_p2.py
- - empirical-sections.md
  - repository-context-layer-paper-v2.html
  - repository-context-layer-paper-v2.pdf
  - title-candidates.md
- - run_claude_code.py
  - run_gemini_cli.py
  - run_pilot.py
- .gitignore
- A-rep1.eval-A-rep1.json
- A-rep1.eval-A2-rep1.json
- A-rep2.eval-A-rep2.json
- A-rep2.eval-A2-rep2.json
- A-rep3.eval-A-rep3.json
- A-rep3.eval-A2-rep3.json
- ARTIFACT.md
- B-rep1.B-smoke.json
- B-rep1.B-smoke2.json
- B-rep1.eval-B-rep1.json
- B-rep1.eval-BT2-rep1.json
- B-rep2.eval-B-rep2.json
- B-rep2.eval-BT2-rep2.json
- B-rep3.eval-B-rep3.json
- B-rep3.eval-BT2-rep3.json
- B-rep4.eval-B-rep4.json
- B-rep4.eval-BT2-rep4.json
- B-rep5.eval-B-rep5.json
- B-rep5.eval-BT2-rep5.json
- C-rep1.eval-C-p11815_12125-rep1.json
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- C-rep1.eval-C-p3305_4687-rep1.json
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- C-rep1.eval-C2-p11066_15252-rep1.json
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- DEVIATIONS-2.md
- DEVIATIONS.md
- Dockerfile
- E-rep1.eval-E-rep1.json
- E-rep1.eval-ET2-rep1.json
- E-rep2.eval-E-rep2.json
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- FINDINGS-01-existence-check.md
- FINDINGS-02-confirmed-transfer-pairs.md
- FINDINGS-03-gemma-pilot.md
- FINDINGS-04-pilot-ab.md
- FINDINGS-05-frontier-pilot.md
- FINDINGS-06-existence-check.md
- FINDINGS-07-qwen-dropped.md
- FINDINGS-08-contract-iteration.md
- Makefile
- PAPER2-DESIGN.md
- PREREGISTRATION-PAPER2.md
- PREREGISTRATION.md
- requirements-artifact.txt
- RESULTS-01-local-tier.md
- RESULTS-02-oracle-frontier.md
- RESULTS-03-frontier-stratified.md
- RESULTS-04-pooled.md
- RESULTS-05-frontier-oracle-tokens.md
- RESULTS-06-delivery-matrix.md
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// repository documentation

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A proposed standard for AI project context: one markdown file, versioned with the code, that agents read before planning and append to after working — under human review.

> Git stores what changed. `context.md` stores what the project knows.

📋 [Spec](SPEC.md) · 📝 [Full example](context.md.example) · 📄 [Whitepaper (PDF)](whitepaper/context-md-manifesto.pdf) · 🎓 [Paper (preprint, PDF)](whitepaper/repository-context-layer-paper.pdf) · 📦 [Archived artifact (DOI)](https://doi.org/10.5281/zenodo.22122045)

## Does it work? Measured.

A pre-registered evaluation on SWE-bench Verified (protocol frozen and timestamped before any run — see [`experiment/`](experiment/)):

<table>
<tr>
<td width="50%" align="center">
 <img src="whitepaper/figure2-lifecycle-effect.png" alt="Slopegraph: a frontier agent resolves 72.9% of held-out constraint-sharing tasks with the consult-execute-learn-promote lifecycle vs 58.3% without it (+14.6pp; raw p=0.041, Holm-adjusted p=0.081), across 11 constraint groups. The result is directional, not confirmed after correction." width="96%"/> 
 A frontier agent running the lifecycle resolves 72.9% of held-out tasks vs 58.3% without it (+14.6&nbsp;pp; raw p&nbsp;=&nbsp;0.041, Holm-adjusted p&nbsp;=&nbsp;0.081; directional).
</td>
<td width="50%" align="center">
 <img src="whitepaper/figure3-capability-gradient.png" alt="Bar chart: an 8B local model's localization improves +26.1pp with frontier-authored context and +21.7pp with gold-distilled context (both p<0.0001), but +0.0pp with its own self-authored lessons." width="96%"/> 
 Context helps a small model only when its author is more capable: frontier-written context +26.1&nbsp;pp (p&nbsp;&lt;&nbsp;0.0001); the model's own lessons +0.0.
</td>
</tr>
</table>

In that regime the observed cost is also lower: **32% fewer tokens per resolved task** — successful fixes cost the same in both arms; the descriptive saving comes from fewer doomed explorations. Because this ratio depends on a directional resolve improvement (raw p = .041, Holm-adjusted p = .081 across the two registered contrasts), treat it as an economic observation in this constraint-sharing sample rather than a confirmed effect.

The reported numbers are recomputed from the artifact data by two checkers: `cd experiment && make reproduce-paper` → `15/15 claims reproduced`, and `python paper/camera-ready-agenticdev2026/analysis/verify_claims.py` → `all checked camera-ready claims verified` (delivery matrix, leakage audit, ceiling and token economics). The protocol, completed-episode transcripts, runner-failure records, audit records, and verdicts are in [`experiment/`](experiment/). An author manually reviewed the fixed 40-episode consultation audit, with 40/40 raw agreement on all three constructs; the artifact discloses that the evidence packet and candidate labels were AI-prepared and the author was not blinded to the detector output. Details are in the [paper](whitepaper/repository-context-layer-paper.pdf).

## The file

```markdown
# Repository Context

## Intent
Local-first CLI. Files are the source of
truth; SQLite is a rebuildable index.

## Constraints
- No ORM. Rejected 2026-03: query opacity
  broke the offline repair path.

## Evolved Context
- [2026-06-29] pkg x >= 3.0 breaks ARM64
  builds. Pin to 2.3.x until fixed.
```

Three sections, all required:

- **Intent** — what the project is, and the design philosophy everything else must serve.
- **Constraints** — the non-negotiable rules, each with its reason. Record the rejection, not just the rule; a rule without a reason is one the agent can comply with but never generalize from.
- **Evolved Context** — an append-only, dated ledger of what agents and humans learned while working here. Entries that prove out get promoted into Constraints by an ordinary reviewed edit.

Agents find the file at a fixed path — `.repo/context.md`, else `context.md` at the repository root — never by searching.

## The contract

```
consult → execute → update → commit
```

1. **Consult.** Read the context before planning.
2. **Execute.** Constraints are binding; Intent breaks ties on open design choices.
3. **Update.** Append what the work taught: the package that breaks the ARM64 build, the proxy timeout nothing documents.
4. **Commit.** Code and updated context travel in one reviewed change, so a human approves both together.

Because the file lives in git, it branches when the code branches, merges when it merges, and rolls back when it rolls back. Conflicting learnings on two branches are an ordinary merge conflict, resolved by a human in review.

## Adopting it today

1. Create `context.md` at your repository root (start from [the example](context.md.example)).
2. Tell your agent to read it before planning and append to **Evolved Context** before committing.
3. Review context diffs like code diffs. Promote proven ledger entries into **Constraints**.

No SDK, no server, no vendor: any agent that can read a file participates, and any human with a text editor is a first-class writer.

## Why not existing artifacts

READMEs describe how to use a project, not how to change it. ADRs are write-once essays no agent is required to read. RAG retrieves by similarity, which fails for constraints — a rule matters most when nothing in the prompt resembles it. IDE memories are private to one tool and invisible to review. Agent instruction files (CLAUDE.md, AGENTS.md) carry orders downward but have no defined way to absorb what the agent learns. What's missing is the combination: versioned with the code, consulted by contract, and written back by the agent under human review. The full argument is in the [whitepaper](whitepaper/context-md-manifesto.pdf); the formal treatment — properties, lifecycle semantics, design analysis, and the reference implementation — is in the [paper](whitepaper/repository-context-layer-paper.pdf).

## Scope

The standard covers discovery and lifecycle, not content. Anything beyond the three headers — decision logs, pattern catalogs, per-directory context — is convention layered on top. Under-specified and adopted beats complete and ignored.

[Metatron](https://github.com/kerbelp/metatron) is one reference implementation: decisions kept as git-backed markdown, served to agents at consult time, feedback routed into the ledger. The abstraction is the point, though — any tool, or none at all, can implement it.

---

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