Build Execution Map

build-execution-map

Analyze a set of GitHub issues for dependency relationships using AI-driven pairwise assessment, then produce a structured execution map JSON artifact that partitions issues into dependency-ordered dispatch groups.

When to Use

• Before parallel dispatch of multiple issues to prevent merge conflicts
• When sous-chef receives a parallel request for N ≥ 2 issues
• As a standalone pre-analysis step before running an implementation campaign

Arguments

Space-separated issue numbers (required, minimum 2), plus optional flags:

• --base-ref — base branch to compare against (default: main)
• --assess-review-approach — assess whether each issue would benefit from a review-approach research pass before implementation (default: inactive)
• --max-parallel — maximum number of issues in any single parallel group (default: 6). Groups exceeding this cap are split into sequential sub-groups of at most N issues each.

Example: 101 103 102 --base-ref main --max-parallel 4 --assess-review-approach

Critical Constraints

NEVER:

• Modify any source code files
• Create files outside {{AUTOSKILLIT_TEMP}}/build-execution-map/ directory (relative to the current working directory)
• Assume issues are independent without analysis
• Launch implementation pipelines — this skill only produces the map
• Use the execution_map or execution_map_report token names with unspaced = (always use key = value format)
• Override the AI's parallelism judgment with mechanical rules
• Assume issues conflict based solely on file-name overlap without reading the issue descriptions
• Run subagents in the background (run_in_background: true is prohibited)
• Treat a medium-severity cross-assessment as grounds for deferral — only critical severity defers
• Emit hasdeferred / deferredcount / dispatched_count with markdown decorators

ALWAYS:

• Use parallel subagents (up to 8) for issue fetching in Step 1
• Use model: "sonnet" for all subagents
• Write both JSON and markdown report outputs to {{AUTOSKILLIT_TEMP}}/build-execution-map/
• Emit execution_map and execution_map_report tokens with absolute paths (use $(pwd) to resolve the working directory prefix)
• Emit structured output tokens as the final lines of text output (plain text, no markdown decorators)
• Check the actual codebase when uncertain whether two issues' changes overlap
• Capture per-pair reasoning in pairwise_assessments for auditability
• Anchor all output paths to the current working directory

Workflow

Step 0 — Parse Arguments

Accept issue numbers as space-separated or comma-separated values. Parse --base-ref if present (default: main). Parse --assess-review-approach if present (default: inactive). When this flag is active, Step 2 will additionally assess each issue for review-approach benefit. Parse --max-parallel if present (default: 6). Validate that it is a positive integer ≥ 1; if a non-positive or non-integer value is provided, abort with "Error: --max-parallel must be a positive integer". Validate the issue count:

• Zero issues: abort immediately with "Error: build-execution-map requires at least 1 issue number" and exit non-zero.
• One issue: emit a warning and write a trivial single-group map (single issue always gets parallel: false).
• Two or more issues: proceed to Step 0.5.

Step 1 — Fetch Issue Data (parallel subagents)

Launch up to 8 parallel sonnet subagents, one per issue. Each subagent:

1. Calls gh issue view {N} --json number,title,body,labels. If the call fails (non-zero

exit — issue not found, auth error, network failure), the subagent must abort and surface the error; do not return a partial or empty result.

1. Returns raw issue data: {"number": N, "title": "..."} — no structured extraction of

affected_files or depends_on. The issue body is read directly by the parent in Step 2.

In the same parallel wave, launch one additional task to fetch the ambient in-progress and queued context:

gh issue list --state open --label "{{github.in_progress_label}}" \
  --json number,title,body,labels,updatedAt --limit 50

gh issue list --state open --label "{{github.queued_label}}" \
  --json number,title,body,labels,updatedAt --limit 50

Use the config value github.in_progress_label (default: "in-progress") and github.queued_label (default: "queued") as the label names. Merge both result sets (deduplicate by issue number) into the ambient context. From the combined list:

• Exclude any issue whose number appears in the current target set — those are being

handled by this session and are already represented in the pairwise assessment.

• Exclude any issue whose labels contain the staged label (github.staged_label,

default: "staged") — staged issues have already landed on the integration branch.

• Treat queued issues with lower default conflict severity than in-progress issues

since they haven't started file modifications yet.

• The remaining issues form the ambient context set.

If the gh issue list call fails (auth error, network failure), log the error and set the in-progress context to an empty list — do not abort the skill. An empty in-progress context is always safe: the skill behaves identically to today.

Do not output any prose between subagent launches — immediately collect results when all subagents complete. All subagents must succeed before advancing to Step 2. If any subagent fails or returns no JSON block, abort the skill with the subagent's error message and the failing issue number.

Step 2 — Assess Parallelism (AI-driven)

Read all issue descriptions holistically. For each pair with potential overlap (shared domain, cross-references, related concerns), determine whether the issues can be safely implemented in parallel.

When uncertain, check the actual codebase: read files, inspect structure, use code-index tools (get_file_summary, get_symbol_body, search_code_advanced) or native Read/Grep.

Produce a per-pair assessment for each pair where a decision is needed:

{
  "pair": [1155, 1156],
  "parallel_safe": true,
  "confidence": "high",
  "reasoning": "#1156 modifies _build_l3_orchestrator_prompt() at lines 70-106. #1155 adds new function at end of file. Different symbols, non-adjacent."
}

Constraints:

• confidence: "low" requires parallel_safe: false (structural, not advisory)
• Pairs that are obviously independent (completely different areas) don't need an assessment

entry — they default to parallel_safe: true; when every pair is obviously independent, pairwise_assessments is an empty array [] (this is valid output)

• Natural language signals ("depends on #X", "can be parallel with #Y") are understood from

issue context, not parsed by regex

• Cross-references in "Files NOT to Change", code blocks, or diagnostic sections are context,

not dependency signals

Step 2b — Cross-Assessment (in-progress context)

When the in-progress context set is non-empty, perform a second assessment pass: for each (target issue, in-progress issue) pair, evaluate conflict potential.

Apply a higher tolerance threshold than pairwise assessment — simple file-level overlap is not sufficient to flag. Target: semantic conflicts, undeclared dependencies, or architectural tensions where implementing the target issue against the pre-in-progress codebase will produce incorrect results or wasted effort. Factor updatedAt recency: issues with no activity in 30+ days carry lower conflict weight — their in-progress label may be stale.

For each pair that requires assessment, produce:

{
  "target_issue": 1155,
  "in_progress_issue": 887,
  "conflict_severity": "low" | "medium" | "critical",
  "conflict_type": "file_overlap" | "semantic_dependency" | "undeclared_dependency" | "architectural_tension",
  "reasoning": "one-sentence explanation",
  "recommendation": "proceed" | "defer" | "escalate"
}

Severity definitions:

• low — Minor file overlap. resolve-merge-conflicts will handle this. Recommendation: proceed. Only record in JSON; suppress from the report to reduce noise.
• medium — Significant overlap or potential indirect dependency, resolvable. Recommendation: proceed with a warning annotation in the report.
• critical — Semantic conflict, undeclared dependency, or architectural tension where implementing the target issue now will produce incorrect results or be discarded. Recommendation: defer.

When the in-progress context is empty, omit Step 2b entirely — cross_assessments is [].

Review-Approach Benefit Assessment (conditional)

When --assess-review-approach is active, perform an additional assessment for each issue after the pairwise parallelism analysis. This assessment determines whether the issue would meaningfully benefit from a review-approach research pass before implementation.

First, read the review-approach skill definition at src/autoskillit/skills_extended/review-approach/SKILL.md to ground your understanding of what that skill actually provides — external web research on modern solutions, approaches, and trade-offs. Do not rely on a hardcoded heuristic list; use the skill definition as the primary reference for what review-approach offers.

Then, for each issue, evaluate whether the problem domain would benefit from that research:

Signals that review-approach would benefit:

• Issue involves integrating an unfamiliar external library or API
• Issue proposes a design decision with multiple viable architectural approaches
• Issue references emerging patterns, standards, or technologies the codebase hasn't used
• Issue body contains open questions about how to approach the problem
• Issue requires understanding trade-offs between competing solutions

Signals that review-approach is NOT needed:

• Issue is a well-scoped bug fix with a clear root cause
• Issue is internal refactoring following established codebase patterns
• Issue adds a feature using patterns already present in the codebase
• Issue is a documentation update or configuration change
• Issue body already contains a fully specified implementation approach

These heuristics are illustrative. Use judgment informed by the actual review-approach SKILL.md to decide each issue.

For each issue, produce:

• review_approach_recommended: true if the issue would benefit, false otherwise
• review_approach_reasoning: one-sentence explanation of why or why not

When --assess-review-approach is NOT active, omit these fields entirely from the output.

Step 3 — Assemble Groups and Merge Order

Using the pairwise assessments from Step 2, partition issues into dispatch groups:

• Issues that are parallel_safe with all other group members go in the same group

(parallel: true)

• Issues with conflicts or dependency order constraints go in separate groups, ordered by

dependency direction (the issue that others depend on goes first)

• Groups with a single issue get parallel: false

Merge order within parallel groups: determined by which changes are most foundational (the AI decides based on issue content — the issue whose changes are most likely to affect others merges last).

The merge_order list is the flattened sequence of issue numbers across groups in dispatch order.

Step 3b — Deferred Issue Routing

After assembling dispatch groups, separate issues flagged as critical in Step 2b:

1. For each target issue that has at least one critical cross-assessment, **remove it from

its dispatch group** and add it to the deferred_issues array.

1. If removing a deferred issue leaves a group empty, remove that group and renumber.
2. Compute:

• deferred_count = count of issues in deferred_issues
• dispatched_count = total_issues − deferred_count
• has_deferred = true if deferred_count > 0, else false

1. Each deferred_issues entry includes blocked_by as an array — a target issue may

have critical conflicts with multiple in-progress issues.

When no cross-assessments produce critical severity, this step is a no-op: deferred_issues is [], has_deferred is false, dispatched_count equals total_issues.

Step 3.5 — Apply Parallel Cap

After groups are assembled in Step 3, enforce the max_parallel cap:

1. For each parallel group (parallel: true) where len(issues) > max_parallel:

• Chunk the issue list into sub-lists of at most max_parallel issues each, preserving

the existing order determined in Step 3.

• Each sub-list becomes its own group. Set parallel: true if len(sub-list) > 1;

set parallel: false if len(sub-list) == 1 (single-issue groups are never parallel).

• The original group's parallel_safe ordering is preserved — do not re-sort.
• Sequential groups (parallel: false) are never split — they are passed through unchanged

regardless of size.

1. Renumber all groups sequentially (1, 2, 3, …) after splitting. If original Group 1

splits into 2 sub-groups and original Group 2 remains intact, the result is: Group 1 (sub-group A of original Group 1), Group 2 (sub-group B of original Group 1), Group 3 (original Group 2).

1. Update merge_order to reflect the new group ordering: issues in Group 1 appear before

Group 2, which appears before Group 3, etc. Within each sub-group, the relative order from Step 3 is preserved.

1. Update group_count to the total count of groups after splitting.

When no group exceeds max_parallel, this step is a no-op — groups pass through unchanged.

Step 4 — Write Output

Compute timestamp {YYYY-MM-DD_HHMMSS} (current local time, second precision).

Create the output directory before writing:

mkdir -p "{{AUTOSKILLIT_TEMP}}/build-execution-map"

If directory creation fails (permission error, missing parent), abort with an explicit error message identifying the failed path — do not silently proceed.

Write two files to {{AUTOSKILLIT_TEMP}}/build-execution-map/ (relative to the current working directory):

1. execution_map_{YYYY-MM-DD_HHMMSS}.json — full structured artifact (schema below)
2. execution_map_report_{YYYY-MM-DD_HHMMSS}.md — human-readable summary including:

• Pairwise assessment table (pair | parallel_safe | confidence | reasoning summary)
• Group assignments table (group | parallel | issues)
• Merge order list
• Any warnings (single-issue shortcut, low-confidence overrides, etc.)
• Review-approach recommendations table (issue | recommended | reasoning) — only when

--assess-review-approach is active

• "## Deferred Issues — Awaiting In-Progress Resolution" section (only when

has_deferred = true) listing: deferred issue number and title, blocking in-progress issue number(s) and title(s), conflict type and reasoning, recommendation

Emit structured output tokens as the last lines of text output:

execution_map = {absolute_path_to_json}
execution_map_report = {absolute_path_to_report}
group_count = {int}
total_issues = {int}
dispatched_count = {int}
deferred_count = {int}
has_deferred = {true|false}
review_approach_candidates = {comma-separated issue numbers}

Emit dispatched_count, deferred_count, and has_deferred unconditionally (even when has_deferred = false and deferred_count = 0).

The review_approach_candidates token is conditional: emit it only when --assess-review-approach is active AND at least one issue has review_approach_recommended: true. The value is a comma-separated list of issue numbers (e.g., 1155,1158). When no issues are recommended or the flag is inactive, omit this token entirely.

Context Limit Behavior

This skill writes output files to {{AUTOSKILLIT_TEMP}}/build-execution-map/ and emits structured output tokens. If context is exhausted mid-execution:

1. Before emitting any structured output tokens, verify that both outpu

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: TalonT-Org
• Source: TalonT-Org/AutoSkillit
• License: MIT

Install and usage instructions live in the source repository linked above.