Ara Research Manager

What it does

When your agent uses it

• AI & LLM Engineering work in your project

Example prompts

• “Use the ara-research-manager skill to record research provenance as a post-task epilogue, scanning conversation history at the end of a coding or…”
• “/ara-research-manager”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Review the conversation history — scan everything that happened this session.
2. 2Extract research-significant events — decisions, experiments, dead ends, claims,
3. 3Read existing ara/ files — get current IDs, existing claims, current tree state.
4. 4Write updates — append new entries to the correct files, update existing entries
5. 5Report what was captured — one-line summary at the end.

What it can do on your machine

Read from SKILL.md and the folder at commit 773a529. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml, markdown and bash).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ara Research Manager loads about 3.1k tokens when it runs, and up to ~7.4k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 797 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~109

When it runs · the whole SKILL.md, loaded when a task matches

~3.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~7.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Orchestra-Research/AI-Research-SKILLs at commit 773a529, republished under its MIT licence (© Orchestra-Research). 797 words, ~3,138 tokens.

name

ara-research-manager

description

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.

version

1.0.0

author

Orchestra Research

license

MIT

tags

ARA, Research Recording, Provenance, Session Logging, Knowledge Management, Exploration Tree, Research Tooling

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

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}"

## 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}

## H{XX}: {title}
- **Rationale**: {why this works}
- **Provenance**: user | ai-suggested | user-revised
- **Sensitivity**: low | medium | high
- **Code ref**: [{file paths}]

- id: O{XX}
  timestamp: "YYYY-MM-DDTHH:MM"
  provenance: user | ai-suggested | ai-executed
  content: "{raw observation}"
  context: "{what was happening}"
  potential_type: claim | heuristic | decision | unknown
  promoted: false

session:
  id: "YYYY-MM-DD_NNN"
  timestamp: "YYYY-MM-DDTHH:MM"
  summary: "{one-line summary of what happened}"

events_logged:
  - type: decision | experiment | dead_end | pivot | claim | heuristic | observation
    id: "{N/C/H/O}{XX}"
    provenance: user | ai-suggested | ai-executed | user-revised
    summary: "{what}"

ai_actions:
  - action: "{what AI did}"
    provenance: ai-executed
    files_changed: ["{paths}"]

claims_touched:
  - id: C{XX}
    action: created | advanced | weakened | confirmed
    provenance: user | ai-suggested

open_threads:
  - "{what needs follow-up}"

ai_suggestions_pending:
  - "{unconfirmed AI suggestions from this session}"

mkdir -p ara/{logic/solution,src/{configs,kernel},trace/sessions,evidence/{tables,figures},staging}

© Orchestra-Research, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in 22-agent-native-research-artifact/research-manager of Orchestra-Research/AI-Research-SKILLs.

• SKILL.md
• references/event-taxonomy.md
• references/provenance-tags.md
• references/session-protocol.md

Open the folder on GitHubat commit 773a529