name	description	user-invocable
scaffold-task	Scaffold a new Harbor-compatible benchmark task (SDLC or org-scale) and optionally a new benchmark suite. Generates task.toml, instruction.md, Dockerfile, test.sh, and registers the task. Triggers on scaffold task, new task, create task, add task, new benchmark.	true

Scaffold Task

Interactively scaffold a new Harbor-compatible benchmark task for CodeScaleBench. Supports both SDLC tasks (code changes in a single repo) and org-scale tasks (cross-repo discovery, compliance, incident triage — artifact-based evaluation). Generates all required files and registers the task in the selection registry.

This is an interactive skill. Walk the user through configuration using AskUserQuestion in multiple phases. Do NOT generate files without first collecting all required inputs.

Phase 1: Mode Selection

Ask one question:

Question 1 — Header: "Mode"

Question: "What would you like to create?"
Options:
- Add SDLC task to existing suite — "Create a new task in an existing csb_sdlc_* benchmark (agent makes code changes)"
- Add org-scale task to existing suite — "Create a new task in an existing csb_org_* benchmark (agent produces an answer artifact)"
- Create new SDLC suite — "Create a new csb_sdlc_* benchmark with its first task and run config"
- Create new org-scale suite — "Create a new csb_org_* benchmark with its first task and run config"

Set {TASK_FAMILY} to either sdlc or org based on the selection.

Phase 2: Core Details

Ask 3-4 questions depending on mode.

If adding to existing SDLC suite:

Question 1 — Header: "Benchmark"

Question: "Which SDLC benchmark suite?"
Options:
- swebenchpro — "Real-world SWE across repos (Go, TS, Python)"
- pytorch — "PyTorch PR-level tasks (Python/C++)"
- locobench — "Long-context understanding (mixed languages)"
- k8sdocs — "Kubernetes documentation (Go)"
Note: User can type "Other" for tac, largerepo, sweperf, crossrepo, dibench

If adding to existing org-scale suite:

Question 1 — Header: "Benchmark"

Question: "Which org-scale benchmark suite?"
Options:
- crossrepo — "Cross-repo dependency tracing"
- crossrepo_tracing — "Cross-repo config/dep tracing with provenance"
- crossorg — "Cross-organization discovery"
- compliance — "Compliance audit tasks"
- migration — "Migration inventory and planning"
- incident — "Incident debug and triage"
- onboarding — "Onboarding comprehension"
- domain — "Domain lineage tasks"
- platform — "Platform knowledge tasks"
- security — "Vulnerability remediation"
- org — "Agentic correctness"

If creating new suite:

Prompt the user (not AskUserQuestion) to provide:

Suite name: Will become csb_{sdlc|org}_{name} (lowercase, alphanumeric + hyphens)

Then ask:

Question 1 — Header: "Language"

Question: "Primary language for this benchmark?"
Options:
- python — "Python 3.11 base image"
- go — "Go 1.23 base image"
- typescript — "Node 20 base image"
- cpp — "GCC 13 base image"

Question 2 — Header: "Difficulty"

Question: "Task difficulty level?"
Options:
- medium — "1-3 files changed, straightforward"
- hard — "4-10 files or complex logic"
- very_hard — "10+ files, deep codebase knowledge"
- expert — "Architectural-level, cross-module"

Question 3 — Header: "Task type"

Question: "How is the task environment set up?"
Options:
- repo-clone — "Clone a git repo at a specific commit (most common)"
- multi-repo-clone — "Clone multiple repos (org-scale tasks)" (org only)
- pre-built-image — "FROM an existing Docker image (e.g., TAC tasks)"
- standalone — "Empty workspace, no repo (agent creates everything)"

For existing suites, also ask Language, Difficulty, and Task type (same questions).

Phase 3: Task-Specific Inputs

Prompt the user for these values (use text prompts, not AskUserQuestion since these are free-form):

SDLC tasks:

Input	Required	Default	Example
Task ID	Yes	—	`my-feature-001`
Description	Yes	—	"Fix race condition in connection pool"
Repo (owner/name)	If repo-clone	—	`pytorch/pytorch`
Commit hash	If repo-clone	—	`ca2466126a00ba8fd877f5a185e40e36ddaceb87`
Base image	If pre-built	—	`ghcr.io/theagentcompany/tac-base:latest`
SDLC phase	Yes	"Implementation (feature)"	See list below
Category	Yes	"feature"	`bug_fix`, `refactoring`, `documentation`, etc.
Time limit (sec)	No	900	`600`

Valid SDLC phases: "Requirements & Discovery", "Architecture & Design", "Implementation (feature)", "Implementation (bug fix)", "Implementation (refactoring)", "Testing & QA", "Documentation", "Maintenance"

Org-scale tasks:

Input	Required	Default	Example
Task ID	Yes	—	`CCX-crossorg-301`
Description	Yes	—	"Trace gRPC service dependency chain across K8s repos"
Repos (owner/name)	Yes (1+)	—	`kubernetes/kubernetes`, `kubernetes/client-go`
Commit/tag per repo	If repo-clone	—	`v1.32.0`
Category	Yes	—	`cross-repo-dep-trace`, `compliance-audit`, etc.
Difficulty	Yes	"hard"	`hard`, `very_hard`, `expert`
Time limit (sec)	No	900	`600`

Phase 4: File Generation

Generate all files using the templates below. Use the Write tool for each file.

Language → Base Image Mapping

Language	Base Image
go	`golang:1.23-bookworm`
python	`python:3.11-bookworm`
cpp	`gcc:13-bookworm`
rust	`rust:1.75-bookworm`
typescript	`node:20-bookworm`
java	`eclipse-temurin:21-bookworm`
c	`gcc:13-bookworm`
csharp	`mcr.microsoft.com/dotnet/sdk:8.0`
mixed	`ubuntu:22.04`

SDLC Templates

Template 1: task.toml (SDLC)

Write to benchmarks/csb_sdlc_{BENCHMARK}/{TASK_ID}/task.toml:

version = "1.0"
[metadata]
name = "{TASK_ID}"
description = "{DESCRIPTION}"
license = "MIT"

[task]
id = "{TASK_ID}"
repo = "{REPO_SHORT_NAME}"
category = "{CATEGORY}"
language = "{LANGUAGE}"
difficulty = "{DIFFICULTY}"
time_limit_sec = {TIME_LIMIT}

[verification]
type = "test"
command = "bash /workspace/tests/test.sh"

[environment]
build_timeout_sec = 1800.0

[environment.setup_scripts]
mcp_config = """#!/bin/bash
# Setup Sourcegraph MCP if credentials provided
if [ -n "$SOURCEGRAPH_ACCESS_TOKEN" ] && [ -n "$SOURCEGRAPH_URL" ]; then
  echo "Setting up Sourcegraph MCP configuration..."
  mkdir -p /root/.config/claude

  cat > /root/.config/claude/mcp.json << 'EOF'
{
  "mcpServers": {
    "sourcegraph": {
      "command": "npx",
      "args": ["-y", "@sourcegraph/mcp-server"],
      "env": {
        "SRC_ACCESS_TOKEN": "$SOURCEGRAPH_ACCESS_TOKEN",
        "SOURCEGRAPH_URL": "$SOURCEGRAPH_URL"
      }
    }
  }
}
EOF

  echo "PASS MCP configuration created"
else
  echo "No Sourcegraph credentials provided, MCP disabled"
fi
exit 0
"""

Notes:

{REPO_SHORT_NAME} is just the repo name without owner (e.g., pytorch from pytorch/pytorch)
For pre-built-image tasks, omit the repo field if there's no git repo
For standalone tasks, omit the repo field

Template 2: Dockerfile (SDLC, repo-clone type)

Write to benchmarks/csb_sdlc_{BENCHMARK}/{TASK_ID}/environment/Dockerfile:

FROM {BASE_IMAGE}

# Install common tools
RUN apt-get update && apt-get install -y \
    git \
    curl \
    ripgrep \
    && rm -rf /var/lib/apt/lists/*

# Install Node.js (for Claude Code CLI)
RUN if ! command -v node &> /dev/null; then \
    curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
    apt-get install -y nodejs; \
    fi

# Install Claude Code CLI
RUN npm install -g @anthropic-ai/claude-code

# Clone repo at pinned commit
RUN git clone --filter=blob:none https://github.com/{REPO}.git /workspace && \
    cd /workspace && \
    git checkout {COMMIT}

# Create directories
RUN mkdir -p /workspace/tests /logs/verifier

# Copy test files
COPY tests/ /workspace/tests/
RUN chmod +x /workspace/tests/test.sh

WORKDIR /workspace

Template 2b: Dockerfile (SDLC, pre-built-image type)

FROM {BASE_IMAGE}

# Create directories
RUN mkdir -p /workspace /logs/verifier

# Copy test files
COPY tests/ /workspace/tests/
RUN chmod +x /workspace/tests/test.sh

WORKDIR /workspace

Template 2c: Dockerfile (SDLC, standalone type)

FROM {BASE_IMAGE}

# Install common tools
RUN apt-get update && apt-get install -y \
    git \
    curl \
    ripgrep \
    && rm -rf /var/lib/apt/lists/*

# Install Node.js (for Claude Code CLI)
RUN if ! command -v node &> /dev/null; then \
    curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
    apt-get install -y nodejs; \
    fi

# Install Claude Code CLI
RUN npm install -g @anthropic-ai/claude-code

# Create directories
RUN mkdir -p /workspace/tests /logs/verifier

# Copy test files
COPY tests/ /workspace/tests/
RUN chmod +x /workspace/tests/test.sh

WORKDIR /workspace

Template 3: instruction.md (SDLC)

Write to benchmarks/csb_sdlc_{BENCHMARK}/{TASK_ID}/instruction.md:

# {TITLE}

- **Repository**: {REPO}
- **Difficulty**: {DIFFICULTY}
- **Category**: {CATEGORY}
- **Task Type**: {TASK_TYPE}

## Description

{DESCRIPTION}

## Task

<!-- Describe the specific work the agent must do -->
YOU MUST IMPLEMENT CODE CHANGES to complete this task.

TODO: Add detailed task instructions here.

## Success Criteria

- [ ] TODO: Define measurable success criteria
- [ ] All changes are committed to the workspace

## Testing

- **Time limit**: {TIME_LIMIT} seconds
- Run `bash /workspace/tests/test.sh` to verify your changes

Notes:

{TITLE} is derived from the task ID: replace hyphens with spaces, title-case
The TODO sections are placeholders for the user to fill in after scaffolding

Template 4: test.sh (SDLC)

Write to benchmarks/csb_sdlc_{BENCHMARK}/{TASK_ID}/tests/test.sh:

#!/bin/bash
# Test script for {TASK_ID}: {DESCRIPTION}

set -e

cd /workspace

# Create log directories
mkdir -p /logs/verifier

# Fix git safe.directory
git config --global --add safe.directory /workspace 2>/dev/null || true

# Guard: if no code changes were made, the agent didn't execute successfully
UNSTAGED_COUNT=$(git diff --stat 2>/dev/null | wc -l)
STAGED_COUNT=$(git diff --cached --stat 2>/dev/null | wc -l)
UNTRACKED_COUNT=$(git ls-files --others --exclude-standard 2>/dev/null | wc -l)
COMMIT_COUNT=0
ORIGIN_REF=""
for ref in origin/master origin/main origin/HEAD; do
    if git rev-parse "$ref" >/dev/null 2>&1; then
        ORIGIN_REF="$ref"
        break
    fi
done
if [ -n "$ORIGIN_REF" ]; then
    COMMIT_COUNT=$(git log --oneline "$ORIGIN_REF..HEAD" 2>/dev/null | wc -l)
elif git rev-parse FETCH_HEAD >/dev/null 2>&1; then
    COMMIT_COUNT=$(git log --oneline FETCH_HEAD..HEAD 2>/dev/null | wc -l)
else
    TOTAL_COMMITS=$(git log --oneline 2>/dev/null | wc -l)
    if [ "$TOTAL_COMMITS" -gt 1 ]; then
        COMMIT_COUNT=$((TOTAL_COMMITS - 1))
    fi
fi
echo "Change detection: unstaged=$UNSTAGED_COUNT staged=$STAGED_COUNT untracked=$UNTRACKED_COUNT commits=$COMMIT_COUNT"
if [ "$UNSTAGED_COUNT" -eq 0 ] && [ "$STAGED_COUNT" -eq 0 ] && [ "$UNTRACKED_COUNT" -eq 0 ] && [ "$COMMIT_COUNT" -eq 0 ]; then
    echo "No code changes detected - agent did not execute successfully"
    echo "0.0" > /logs/verifier/reward.txt
    echo ""
    echo "Tests completed - Score: 0.0 (no changes)"
    exit 0
fi

# ── Scoring ──────────────────────────────────────────────
SCORE=0
MAX_SCORE=10

# TODO: Add verification checks here. Examples:
#
# Check 1: Required file exists (2 points)
# if [ -f "path/to/expected/file" ]; then
#     echo "PASS: Expected file exists"
#     SCORE=$((SCORE + 2))
# else
#     echo "FAIL: Expected file not found"
# fi
#
# Check 2: File contains expected pattern (2 points)
# if grep -q "expected_pattern" "path/to/file"; then
#     echo "PASS: Expected pattern found"
#     SCORE=$((SCORE + 2))
# else
#     echo "FAIL: Expected pattern not found"
# fi

echo "WARNING: Using placeholder scoring - customize this test script"
SCORE=$MAX_SCORE  # Remove this line after adding real checks

# Convert to decimal score (0.0 - 1.0)
FINAL_SCORE=$(awk "BEGIN {printf \"%.1f\", $SCORE / $MAX_SCORE}")

echo "$FINAL_SCORE" > /logs/verifier/reward.txt
echo ""
echo "Tests completed - Score: $FINAL_SCORE (${SCORE}/${MAX_SCORE} checks passed)"

Template 5: reviewers.json (SDLC — also applies to org-scale tasks)

Write to benchmarks/csb_sdlc_{BENCHMARK}/{TASK_ID}/reviewers.json:

After creating the task directory, generate a reviewers.json by querying GitHub for contributor and reviewer information. Use the backfill script or query the API directly:

# Option A: Use the backfill script for a single task
python3 scripts/backfill_reviewers.py --task-dir benchmarks/csb_sdlc_{BENCHMARK}/{TASK_ID}

# Option B: Manual generation via gh CLI
gh api "repos/{REPO}/commits?path={CODE_AREA}&per_page=30" --jq '.[].author.login' | sort | uniq -c | sort -rn | head -5

If the task was mined from a specific PR (Phase 3), include the full PR metadata:

{
  "task_id": "{TASK_ID}",
  "repos": ["{REPO}"],
  "source_pr": {
    "number": {PR_NUMBER},
    "url": "https://github.com/{REPO}/pull/{PR_NUMBER}",
    "author": "{PR_AUTHOR}",
    "merged_by": "{MERGER}",
    "reviewers": ["{REVIEWER1}", "{REVIEWER2}"]
  },
  "top_contributors": [
    {"login": "{CONTRIBUTOR1}", "commits": {N}},
    {"login": "{CONTRIBUTOR2}", "commits": {M}}
  ],
  "code_areas": ["{DIR1}/", "{DIR2}/"],
  "suggested_reviewers": ["{REVIEWER1}", "{CONTRIBUTOR1}", "{PR_AUTHOR}"],
  "discovery_method": "source_pr"
}

If no source PR is available, use the git log frequency method:

{
  "task_id": "{TASK_ID}",
  "repos": ["{REPO}"],
  "top_contributors": [
    {"login": "{CONTRIBUTOR1}", "commits": {N}},
    {"login": "{CONTRIBUTOR2}", "commits": {M}}
  ],
  "code_areas": ["{DIR1}/", "{DIR2}/"],
  "suggested_reviewers": ["{CONTRIBUTOR1}", "{CONTRIBUTOR2}", "{CONTRIBUTOR3}"],
  "discovery_method": "git_log_frequency"
}

Exclude bot accounts from all lists: dependabot, renovate, bors, k8s-ci-robot, copybara-service, etc.

Org-Scale Templates

Template 1: task.toml (org-scale)

Write to benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/task.toml:

version = "1.0"

[metadata]
name = "{TASK_ID}"
description = "{DESCRIPTION}"
license = "Apache-2.0"

[task]
id = "{TASK_ID}"
repo = "{PRIMARY_REPO}"
category = "{CATEGORY}"
language = "{LANGUAGE}"
difficulty = "{DIFFICULTY}"
time_limit_sec = {TIME_LIMIT}
mcp_suite = "csb_org_{BENCHMARK}"
org_scale = true
verification_modes = ["artifact"]

[verification]
type = "test"
command = "bash /tests/test.sh"
reward_type = "score"
description = "{DESCRIPTION}"

[environment]
build_timeout_sec = 600.0

Notes:

{PRIMARY_REPO} uses the sg-evals mirror format if available (e.g., sg-evals/kubernetes--v1.32.0), otherwise owner/repo
verification.command uses /tests/test.sh (NOT /workspace/tests/test.sh — Harbor uploads tests to /tests/)
org_scale = true marks this as an organizational use-case benchmark

Template 2: Dockerfile (org-scale, multi-repo-clone)

Write to benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/environment/Dockerfile:

FROM {BASE_IMAGE}

# Install common tools
RUN apt-get update && apt-get install -y \
    git curl jq ripgrep python3 python3-pip \
    && rm -rf /var/lib/apt/lists/*

# Install Node.js (for Claude Code CLI)
RUN if ! command -v node &> /dev/null; then \
    curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
    apt-get install -y nodejs; \
    fi

# Install Claude Code CLI
RUN npm install -g @anthropic-ai/claude-code

# Clone repos at pinned versions
{CLONE_COMMANDS}

# Create directories
RUN mkdir -p /workspace /tests /logs/verifier

COPY tests/ /tests/
RUN chmod +x /tests/test.sh /tests/eval.sh 2>/dev/null || true

WORKDIR /workspace

For each repo, generate a clone command like:

RUN git clone --filter=blob:none https://github.com/{REPO}.git /workspace/{REPO_DIR} && \
    cd /workspace/{REPO_DIR} && \
    git checkout {COMMIT_OR_TAG}

Where {REPO_DIR} is derived from the repo name (e.g., kubernetes from kubernetes/kubernetes).

Template 2b: Dockerfile (org-scale, single repo-clone)

Same as multi-repo but with a single clone command.

Template 3: instruction.md (org-scale)

Write to benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/instruction.md:

# {TITLE}

## Context

You have access to the following repositories in `/workspace/`:

{REPO_LIST}

## Task

{DESCRIPTION}

## Deliverable

Write your answer to `/workspace/answer.json` with the following structure:

```json
{
  "task_id": "{TASK_ID}",
  "findings": [
    {
      "description": "TODO: describe finding",
      "files": ["path/to/relevant/file"],
      "evidence": "TODO: supporting evidence"
    }
  ]
}

Constraints

Time limit: {TIME_LIMIT} seconds
Your answer MUST be valid JSON written to /workspace/answer.json
Be thorough — recall matters more than precision


Notes:
- `{REPO_LIST}` is a bulleted list of repos with their paths, e.g., `- kubernetes/kubernetes → /workspace/kubernetes`
- Org-scale instructions must be **tool-neutral** — do NOT mention MCP, Sourcegraph, or specific tools. Both baseline and MCP agents must be able to solve the task.

#### Template 4: eval.sh (org-scale)

Write to `benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/tests/eval.sh`:

```bash
#!/bin/bash
# eval.sh — org-scale benchmark evaluator for {TASK_ID}
# Exit-code-first (SWE-Factory pattern):
#   exit 0 — agent produced useful output (composite score > 0)
#   exit 1 — total failure (composite score == 0 or missing answer)
#
# Writes /logs/verifier/reward.txt with the composite score [0.0, 1.0]

set -euo pipefail

TASK_ID="{TASK_ID}"
ANSWER_PATH="/workspace/answer.json"
TASK_SPEC_PATH="/tests/task_spec.json"
ORACLE_CHECKS="/tests/oracle_checks.py"
REWARD_PATH="/logs/verifier/reward.txt"

mkdir -p /logs/verifier

echo "=== {TASK_ID} evaluator ==="
echo "Task spec: $TASK_SPEC_PATH"

# --- Guard: answer.json must exist and be valid JSON ---
if [ ! -f "$ANSWER_PATH" ]; then
    echo "FAIL: $ANSWER_PATH not found"
    echo "0.0" > "$REWARD_PATH"
    exit 1
fi
if ! python3 -c "import json; json.load(open('$ANSWER_PATH'))" 2>/dev/null; then
    echo "FAIL: $ANSWER_PATH is not valid JSON"
    echo "0.0" > "$REWARD_PATH"
    exit 1
fi

# --- Run oracle checks ---
if [ -f "$ORACLE_CHECKS" ] && [ -f "$TASK_SPEC_PATH" ]; then
    SCORE=$(python3 "$ORACLE_CHECKS" "$TASK_SPEC_PATH" "$ANSWER_PATH" 2>&1) || true
    echo "Oracle score: $SCORE"
    echo "$SCORE" > "$REWARD_PATH"
else
    echo "WARNING: oracle_checks.py or task_spec.json missing, using placeholder"
    echo "0.5" > "$REWARD_PATH"
fi

FINAL=$(cat "$REWARD_PATH")
echo "Final score: $FINAL"

if [ "$(echo "$FINAL > 0" | bc -l 2>/dev/null || echo 0)" = "1" ]; then
    exit 0
else
    exit 1
fi

Template 5: test.sh (org-scale)

Write to benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/tests/test.sh:

#!/bin/bash
# test.sh — wrapper that delegates to eval.sh
exec bash /tests/eval.sh "$@"

Template 6: oracle_checks.py (org-scale)

Write to benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/tests/oracle_checks.py:

#!/usr/bin/env python3
"""Deterministic oracle check library for org-scale benchmark evaluation.

Provides reusable check functions that eval.sh scripts invoke to score agent
answers against closed-world oracle definitions. Returns raw scores (no
rounding) so the caller controls final precision.
"""

import json
import sys


def main():
    if len(sys.argv) < 3:
        print("Usage: oracle_checks.py <task_spec.json> <answer.json>", file=sys.stderr)
        print("0.0")
        sys.exit(0)

    with open(sys.argv[1]) as f:
        spec = json.load(f)
    with open(sys.argv[2]) as f:
        answer = json.load(f)

    # TODO: Implement oracle checks against spec
    # Example: check that required files/symbols are found
    checks = spec.get("evaluation", {}).get("checks", [])
    if not checks:
        print("0.5")  # No checks defined yet
        return

    passed = 0
    total = len(checks)
    for check in checks:
        # TODO: implement check logic
        passed += 1

    score = passed / total if total > 0 else 0.0
    print(f"{score:.4f}")


if __name__ == "__main__":
    main()

Template 7: task_spec.json (org-scale)

Write to benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/tests/task_spec.json:

{
  "task_id": "{TASK_ID}",
  "evaluation": {
    "checks": []
  }
}

Note: The checks array should be populated with oracle checks after the task is authored. Use scripts/generate_csb_org_tasks.py or manual curation.

Phase 5: Registration

Add to selected_benchmark_tasks.json

Read configs/selected_benchmark_tasks.json, then use Edit to append a new task entry to the tasks array (before the closing ]).

SDLC registration entry:

{
  "task_id": "{TASK_ID}",
  "benchmark": "csb_sdlc_{BENCHMARK}",
  "sdlc_phase": "{SDLC_PHASE}",
  "language": "{LANGUAGE}",
  "difficulty": "{DIFFICULTY}",
  "category": "{CATEGORY}",
  "repo": "{REPO}",
  "mcp_benefit_score": 0.5,
  "mcp_breakdown": {
    "context_complexity": 0.5,
    "cross_file_deps": 0.5,
    "semantic_search_potential": 0.5,
    "task_category_weight": 0.5
  },
  "selection_rationale": "Manually added via /scaffold-task",
  "task_dir": "csb_sdlc_{BENCHMARK}/{TASK_ID}"
}

Org-scale registration entry:

{
  "task_id": "{TASK_ID}",
  "benchmark": "csb_org_{BENCHMARK}",
  "language": "{LANGUAGE}",
  "difficulty": "{DIFFICULTY}",
  "category": "{CATEGORY}",
  "repo": "{PRIMARY_REPO}",
  "selection_rationale": "Manually added via /scaffold-task",
  "task_dir": "csb_org_{BENCHMARK}/{TASK_ID}"
}

Also update the metadata.total_selected count and the statistics.tasks_per_benchmark count for the appropriate suite.

If new suite: Generate run config script

Write to configs/{BENCHMARK}_2config.sh using the standard 2-config pattern. Read an existing config (e.g., configs/tac_2config.sh) as a reference and adapt it:

Source _common.sh
Define SUITE="csb_{sdlc|org}_{BENCHMARK}"
Load task IDs from selected_benchmark_tasks.json filtered by benchmark
Define run_task_batch() with baseline, sourcegraph_full configs
Run the 2 batches sequentially
Make it executable: chmod +x configs/{BENCHMARK}_2config.sh

Phase 6: Validation

After all files are created, run validation:

cd ~/CodeScaleBench && python3 scripts/validate_tasks_preflight.py --task benchmarks/csb_{sdlc|org}_{BENCHMARK}/{TASK_ID}

Report the validation results. If there are issues, offer to fix them.

Summary Output

After completion, print a summary:

Scaffolded task: {TASK_ID}
  Suite:      csb_{sdlc|org}_{BENCHMARK}
  Family:     {sdlc|org-scale}
  Language:   {LANGUAGE}
  Difficulty: {DIFFICULTY}
  Type:       {TASK_TYPE}

Files created:
  benchmarks/csb_{sdlc|org}_{BENCHMARK}/{TASK_ID}/task.toml
  benchmarks/csb_{sdlc|org}_{BENCHMARK}/{TASK_ID}/instruction.md
  benchmarks/csb_{sdlc|org}_{BENCHMARK}/{TASK_ID}/reviewers.json
  benchmarks/csb_{sdlc|org}_{BENCHMARK}/{TASK_ID}/environment/Dockerfile
  benchmarks/csb_{sdlc|org}_{BENCHMARK}/{TASK_ID}/tests/test.sh
  [org-scale only] benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/tests/eval.sh
  [org-scale only] benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/tests/oracle_checks.py
  [org-scale only] benchmarks/csb_org_{BENCHMARK}/{TASK_ID}/tests/task_spec.json

Registered in: configs/selected_benchmark_tasks.json

Next steps:
  1. Edit instruction.md with detailed task instructions
  2. Customize tests/ with task-specific verification checks
  3. [org-scale] Populate task_spec.json with oracle checks
  4. Test locally: harbor run --path benchmarks/csb_{sdlc|org}_{BENCHMARK}/{TASK_ID}
  5. Run /validate-tasks --task benchmarks/csb_{sdlc|org}_{BENCHMARK}/{TASK_ID}

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Scaffold Task

Phase 1: Mode Selection

Phase 2: Core Details

If adding to existing SDLC suite:

If adding to existing org-scale suite:

If creating new suite:

Phase 3: Task-Specific Inputs

SDLC tasks:

Org-scale tasks:

Phase 4: File Generation

Language → Base Image Mapping

SDLC Templates

Template 1: task.toml (SDLC)

Template 2: Dockerfile (SDLC, repo-clone type)

Template 2b: Dockerfile (SDLC, pre-built-image type)

Template 2c: Dockerfile (SDLC, standalone type)

Template 3: instruction.md (SDLC)

Template 4: test.sh (SDLC)

Template 5: reviewers.json (SDLC — also applies to org-scale tasks)

Org-Scale Templates

Template 1: task.toml (org-scale)

Template 2: Dockerfile (org-scale, multi-repo-clone)

Template 2b: Dockerfile (org-scale, single repo-clone)

Template 3: instruction.md (org-scale)

Constraints

Template 5: test.sh (org-scale)

Template 6: oracle_checks.py (org-scale)

Template 7: task_spec.json (org-scale)

Phase 5: Registration

Add to selected_benchmark_tasks.json

SDLC registration entry:

Org-scale registration entry:

If new suite: Generate run config script

Phase 6: Validation

Summary Output

FilesExpand file tree

SKILL.md

Latest commit

History

SKILL.md

File metadata and controls

Scaffold Task

Phase 1: Mode Selection

Phase 2: Core Details

If adding to existing SDLC suite:

If adding to existing org-scale suite:

If creating new suite:

Phase 3: Task-Specific Inputs

SDLC tasks:

Org-scale tasks:

Phase 4: File Generation

Language → Base Image Mapping

SDLC Templates

Template 1: task.toml (SDLC)

Template 2: Dockerfile (SDLC, repo-clone type)

Template 2b: Dockerfile (SDLC, pre-built-image type)

Template 2c: Dockerfile (SDLC, standalone type)

Template 3: instruction.md (SDLC)

Template 4: test.sh (SDLC)

Template 5: reviewers.json (SDLC — also applies to org-scale tasks)

Org-Scale Templates

Template 1: task.toml (org-scale)

Template 2: Dockerfile (org-scale, multi-repo-clone)

Template 2b: Dockerfile (org-scale, single repo-clone)

Template 3: instruction.md (org-scale)

Constraints

Template 5: test.sh (org-scale)

Template 6: oracle_checks.py (org-scale)

Template 7: task_spec.json (org-scale)

Phase 5: Registration

Add to selected_benchmark_tasks.json

SDLC registration entry:

Org-scale registration entry:

If new suite: Generate run config script

Phase 6: Validation

Summary Output