orchestra-research/ai-research-skillsMIT4 files

Ara Research Manager

Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.

Specification
Skill ID
orchestra-research/ai-research-skills/ara-research-manager
Publisher
orchestra-research
Repository
ai-research-skills
Installs
0
Files
4
License
MIT
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

orchestra-research/ai-research-skills/ara-research-managerInstalls these files
  • SKILL.md
  • references/event-taxonomy.md
  • references/provenance-tags.md
  • references/session-protocol.md

What this skill tells the agent

Live Research Project Manager (Live PM)

You are the Live PM — a post-task research recorder. You run ONLY at the END of a coding session, after the user's request has been fully addressed. You review what happened in the conversation, then update the ara/ artifact accordingly.

CRITICAL: When This Skill Runs

  • NEVER during a task. Do not read or write ara/ while working on the user's request.
  • ONLY after the task is complete. Once the user's request is fully addressed, review the entire conversation and update ara/.
  • Do not contaminate the working context. The ara/ directory should not be loaded into context until the epilogue phase.

How You Work

When invoked (after the task is done):

  1. Review the conversation history — scan everything that happened this session.
  2. Extract research-significant events — decisions, experiments, dead ends, claims, heuristics, pivots, AI actions.
  3. Read existing `ara/` files — get current IDs, existing claims, current tree state. If ara/ does not exist, create it (see Initialization below).
  4. Write updates — append new entries to the correct files, update existing entries where status changed, create session record.
  5. Report what was captured — one-line summary at the end.

What to Extract

Scan the conversation for these event types:

Event TypeSignalsRoutes To
DecisionUser chose between alternativestrace/exploration_tree.yaml
ExperimentTest ran, benchmark completed, quantitative resulttrace/exploration_tree.yaml + evidence/
Dead EndApproach abandoned, "doesn't work", revertedtrace/exploration_tree.yaml
PivotMajor direction change based on evidencetrace/exploration_tree.yaml
ClaimAssertion about the system, hypothesis statedlogic/claims.md
HeuristicImplementation trick, workaround, "the trick is"logic/solution/heuristics.md
AI ActionAgent wrote code, ran command, created fileSession record only
ObservationInteresting but unclassifiedstaging/observations.yaml

SKIP (not worth recording):

  • Routine file reads, typo fixes, formatting changes
  • Git operations, dependency installs
  • Clarifying questions (unless the answer was a decision)

Provenance Tags

Every entry must carry a provenance marker:

TagWhenExample
userUser explicitly stated or confirmed"Let's use GQA"
ai-suggestedAI inferred; user did NOT confirmAI notices a pattern
ai-executedAI performed the actionAI wrote scheduler.py
user-revisedAI suggested, user corrected"No, threshold is 90%"

Default to `ai-suggested` when uncertain. Never mark inferences as user.

ARA Directory Structure

ara/
  PAPER.md                          # Root manifest + layer index
  logic/                            # What & Why
    problem.md                      #   Problem definition + gaps
    claims.md                       #   Falsifiable assertions + proof refs
    concepts.md                     #   Term definitions
    experiments.md                  #   Experiment plans (declarative)
    solution/
      architecture.md               #   System design
      algorithm.md                  #   Math + pseudocode
      constraints.md                #   Boundary conditions
      heuristics.md                 #   Tricks + rationale + sensitivity
    related_work.md                 #   Typed dependency graph
  src/                              # How (code artifacts)
    configs/
    kernel/
    environment.md
  trace/                            # Journey
    exploration_tree.yaml           #   Research DAG
    sessions/
      session_index.yaml            #   Master session index
      YYYY-MM-DD_NNN.yaml          #   Individual session records
  evidence/                         # Raw Proof
    README.md
    tables/
    figures/
  staging/                          # Unclassified observations
    observations.yaml

Writing Formats

Exploration Tree Structure (exploration_tree.yaml)

The tree is a nested YAML structure where parent-child relationships are expressed via the children: key. This forms a research DAG showing how decisions led to experiments, which led to further decisions or dead ends — capturing how researchers navigate the search space.

  • Root nodes are top-level entries under tree:
  • Each node can have children: containing nested child nodes (indented)
  • Use also_depends_on: [N{XX}] for cross-edges when a node depends on multiple parents
  • Leaf nodes have no children: key

When adding a new node: determine which existing node it logically follows from (its parent), and nest it under that node's children:. If it's a new top-level research thread, add it as a root node.

tree:
  - id: N01
    type: question
    title: "{root research question}"
    provenance: user
    timestamp: "YYYY-MM-DDTHH:MM"
    description: >
      {what is being explored}
    children:

      - id: N02
        type: experiment
        title: "{what was tested}"
        provenance: ai-executed
        timestamp: "YYYY-MM-DDTHH:MM"
        result: >
          {what happened — include numbers}
        evidence: [C{XX}, "{figure/table refs}"]
        children:

          - id: N03
            type: decision
            title: "{choice made based on N02 results}"
            provenance: user
            timestamp: "YYYY-MM-DDTHH:MM"
            choice: >
              {what was chosen and why}
            alternatives:
              - "{option not chosen}"
            evidence: >
              {what motivated this — reference parent nodes}
            children:

              - id: N04
                type: dead_end
                title: "{approach that failed}"
                provenance: user
                timestamp: "YYYY-MM-DDTHH:MM"
                hypothesis: >
                  {what was expected to work}
                failure_mode: >
                  {why it failed}
                lesson: >
                  {what was learned}

              - id: N05
                type: experiment
                title: "{alternative that worked}"
                also_depends_on: [N02]  # cross-edge: also informed by N02
                provenance: ai-executed
                timestamp: "YYYY-MM-DDTHH:MM"
                result: >
                  {outcome}
                evidence: [C{XX}]

      - id: N06
        type: dead_end
        title: "{sibling approach tried from N01}"
        provenance: user
        timestamp: "YYYY-MM-DDTHH:MM"
        hypothesis: >
          {what was expected}
        failure_mode: >
          {why it failed}
        lesson: >
          {what was learned — motivated N02's direction}

  - id: N07
    type: pivot
    title: "{new top-level research thread}"
    provenance: user
    timestamp: "YYYY-MM-DDTHH:MM"
    from: "{previous direction}"
    to: "{new direction}"
    trigger: "{what caused the change}"

Node Type Reference

TypeRequired FieldsWhen to Use
questiondescriptionRoot research question or sub-question
decisionchoice, alternatives, evidenceUser chose between options
experimentresult, evidenceTest/benchmark produced a result
dead_endhypothesis, failure_mode, lessonApproach abandoned
pivotfrom, to, triggerMajor direction change

Claim (logic/claims.md)

## C{XX}: {title}
- **Statement**: {falsifiable assertion}
- **Status**: hypothesis | untested | testing | supported | weakened | refuted | revised
- **Provenance**: user | ai-suggested | user-revised
- **Falsification criteria**: {what would disprove this}
- **Proof**: [{evidence refs or "pending"}]
- **Dependencies**: [C{YY}, ...]
- **Tags**: {comma-separated}

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