nightshift

name	nightshift
description	Autonomous overnight code quality bot. 69 tasks across 7 categories with plan-implement-review architecture. 26 PR tasks + 43 issue tasks.
trigger	nightshift

Nightshift v3

Full implementation of marcus/nightshift for Hermes Agent. 69 task types, plan-implement-review architecture, budget tracking. 26 PR tasks + 43 issue tasks.

Setup

• State: ~/.nightshift/ (config + run history)
• Workspace: ~/nightshift-workspace/ (temp clones)
• Script: ~/nightshift-workspace/nightshift.py
• Task defs: ~/nightshift-workspace/nightshift_tasks.json
• Quota: ~/nightshift-workspace/glm_quota.py
• Model: GLM 5.1 via api.z.ai (model ID: glm-5.1, verified working on the Api.z.ai provider)
• Cron: fd3b3a346a68 (every 15 min, Discord delivery)

Commands

python3 ~/nightshift-workspace/nightshift.py --list-categories  # 7 categories
python3 ~/nightshift-workspace/nightshift.py --list-tasks       # All 61 tasks
python3 ~/nightshift-workspace/nightshift.py --dry-run          # Preview
python3 ~/nightshift-workspace/nightshift.py                    # Select + clone + output
python3 ~/nightshift-workspace/glm_quota.py --check             # Quota gate

Task Categories (7 categories, 69 tasks)

Category	Tasks	Output Mode	Review Loop
pr	24	PR (24)	Yes
analysis	18	Issue	No
options	13	Issue	No
safe	5	Issue	No
map	7	PR (1) + Issue (6)	Yes (1 task)
emergency	3	Issue	No
review	1	PR	Yes

26 tasks create PRs (actual code/artifact changes). 43 tasks create issues (findings, reports, suggestions). The output_mode field determines the output type, independent of the task category.

PR Tasks (26)

lint-fix, bug-finder, auto-dry, skill-groom, api-contract-verify, backward-compat, build-optimize, docs-backfill, commit-normalize, changelog-synth, release-notes, adr-draft, ci-fixes, dependency-updates, readme-improvements, dead-code, code-quality, code-review, visibility-instrument, perf-audit, autoresearch, react-effect-cleanup, react-image-fix, lint-doctor-fix, best-practice-fix, optimo

Issue Tasks (43)

doc-drift, afdocs-scan, semantic-diff, dependency-risk, test-gap, test-flakiness, logging-audit, metrics-coverage, perf-regression, cost-attribution, security-footgun, pii-scanner, privacy-policy, schema-evolution, event-taxonomy, roadmap-entropy, bus-factor, knowledge-silo, tech-debt-classify, why-annotator, edge-case-enum, error-msg-improve, slo-suggester, ux-copy-sharpener, a11y-lint, service-advisor, ownership-boundary, oncall-estimator, idea-generator, migration-rehearsal, contract-fuzzer, golden-path, perf-profile, allocation-profile, repo-topology, permissions-mapper, data-lifecycle, feature-flag-monitor, ci-signal-noise, historical-context, runbook-gen, rollback-plan, postmortem-gen

Category Breakdown

PR category (24): lint-fix, bug-finder, auto-dry, skill-groom, api-contract-verify, backward-compat, build-optimize, docs-backfill, commit-normalize, changelog-synth, release-notes, adr-draft, ci-fixes, dependency-updates, readme-improvements, dead-code, code-quality, perf-audit, autoresearch, react-effect-cleanup, react-image-fix, lint-doctor-fix, best-practice-fix, optimo

Analysis category (18): doc-drift, afdocs-scan, semantic-diff, dependency-risk, test-gap, test-flakiness, logging-audit, metrics-coverage, perf-regression, cost-attribution, security-footgun, pii-scanner, privacy-policy, schema-evolution, event-taxonomy, roadmap-entropy, bus-factor, knowledge-silo

Options category (13): task-groomer, guide-improver, tech-debt-classify, why-annotator, edge-case-enum, error-msg-improve, slo-suggester, ux-copy-sharpener, a11y-lint, service-advisor, ownership-boundary, oncall-estimator, idea-generator

Safe category (5): migration-rehearsal, contract-fuzzer, golden-path, perf-profile, allocation-profile

Map category (7): visibility-instrument (PR), repo-topology, permissions-mapper, data-lifecycle, feature-flag-monitor, ci-signal-noise, historical-context

Emergency category (3): runbook-gen, rollback-plan, postmortem-gen

Review category (1): code-review — Expert review + fixes using SOLID, security, perf, error handling checklists (references/ directory). Creates PR with fixes.

Architecture: Plan-Implement-Review

From nightshift orchestrator.go. Three prompt templates:

Phase 1: Plan

Agent analyzes the repo and outputs JSON:

{"steps": ["step1", "step2"], "files": ["file1.go"], "description": "approach"}

Phase 2: Implement

Agent executes the plan. On iteration >1, includes review feedback. Outputs JSON:

{"files_modified": ["file1.go"], "summary": "what was done"}

Phase 3: Review

Agent checks correctness. Outputs JSON:

{"passed": true, "feedback": "detailed feedback", "issues": ["issue1"]}

If review fails → back to implement with feedback (max 3 iterations).

Cost Tiers (token estimation)

Tier	Tokens	Example Tasks
low	10-50k	lint-fix, ci-fixes, readme, changelog
medium	50-150k	bug-finder, dead-code, test-gap, most analysis
high	150-500k	auto-dry, build-optimize, service-advisor
very_high	500k-1M	migration-rehearsal, contract-fuzzer

Execution Flow (Cron Agent)

STEP 0 — QUOTA

python3 ~/nightshift-workspace/glm_quota.py --check

If SKIP → report and stop.

STEP 1 — DISCOVER

python3 ~/nightshift-workspace/nightshift.py

Parse NIGHTSHIFT_TASKS_START/NIGHTSHIFT_TASKS_END JSON array. Each task has:

• repo, task, task_name, task_description, category, cost_tier, cost_tokens_min/max
• output_mode: "pr" (creates pull request) or "issue" (creates GitHub issue)
• prompt_type: "plan_implement_review" | "analysis"
• clone_dir, default_branch, branch_name, max_review_iterations, produces_pr, has_review

STEP 2 — EXECUTE EACH TASK

output_mode = "pr" (21 tasks):

prompt_type = "plan_implement_review":

1. Plan — delegate_task: "Analyze {clone_dir} and create a plan for {task_name}. {task_description}. Output JSON with steps, files, description."
2. Implement — delegate_task: "Execute plan in {clone_dir}. {task_description}. Rules: no formatters, no new deps, targeted changes only."
3. Check — git status --porcelain. If empty → "no changes", continue.
4. Review — delegate_task: "Review git diff in {clone_dir}. Check correctness, security, unintended changes. Output JSON: {passed, feedback, issues}"
5. If failed and iterations < max → implement again with feedback.
6. PR — Use: git -c user.name="Nightshift" -c user.email="[e-mail]" commit. Must use --head BRANCH_NAME because the main agent's working directory may not be on the feature branch (subagent commits on it).

code-review task (plan_implement_review):

• Load references/ checklists (solid-checklist.md, security-checklist.md, code-quality-checklist.md)
• Plan: analyze codebase using checklists, identify issues
• Implement: fix identified issues (SOLID violations, security risks, error handling gaps)
• Review: verify fixes are correct and don't introduce new issues
• Write findings as markdown, create PR with fixes and report

output_mode = "issue" (43 tasks):

• analysis prompt_type: delegate_task with analysis prompt.
• Write findings to a structured markdown string.
• Create a GitHub issue (NOT a PR): GH_TOKEN=<NIGHTSHIFT_GH_TOKEN> gh issue create --repo {repo} --title "nightshift: {task_name}" --body {findings_markdown}
• Issue body should include: severity levels, file paths, actionable recommendations.
• Do NOT commit any files to the repo. Do NOT create a branch for issue tasks.

Git commit author (PR tasks only): Nightshift <[e-mail]> (use -c user.name="Nightshift" -c user.email="[e-mail]").

STEP 3 — CLEANUP

import shutil; shutil.rmtree("{clone_dir}")

STEP 4 — REPORT

For each task: repo, task_name, category, output_mode, result (PR URL / Issue URL / "no changes" / "review failed"). PR tasks should have a PR URL. Issue tasks should have an issue URL.

Budget Tracking

• Per-task cost estimation (cost_tier → token range)
• Quota check via glm_quota.py (--check flag)
• Dynamic burn window: only runs 5-50 min before quota reset
• Configurable budget reserve (default 20%)
• Cost tier cap in config (max_cost_tier)

Quota API (glm_quota.py)

• Host: open.bigmodel.cn, auth: Authorization: <GLM_API_KEY> (no Bearer prefix)
• GET /api/monitor/usage/quota/limit — returns reset_utc in TOKENS_LIMIT
• The burn window is calculated from reset_utc — not a fixed time
• Source: openclaw-glm-plan-usage

Daily Brief Integration

The GitHub monitor script (~/alive/scripts/github_monitor.py) fetches open [nightshift] PRs and appends them to ~/alive/github/YYYY-MM-DD.md under a "Nightshift PRs" section. This feeds into the daily brief automatically.

GLM 5.1 Model

Verified working on api.z.ai with the regular GLM_API_KEY. Model ID: glm-5.1. The coding plan supports switching models via Claude Code settings (see https://docs.z.ai/devpack/using5.1).

Cron Job

• ID: fd3b3a346a68
• Schedule: */15 * * * * (every 15 min — but only actually runs during burn window)
• Delivery: Local (cron output only)
• Model: glm-5.1 (via api.z.ai, regular GLM_API_KEY, no Bearer prefix)
• Tasks/run: 3 (configurable)
• Repeat: forever
• Git author: Nightshift <[e-mail]> (all commits)
• Fork filter: Enabled — skips repos where isFork=true
• Public-only filter: Enabled — skips private repos (public_only=true)
• Inactivity filter: Enabled — skips repos with no pushes in last 30 days (max_inactive_days=30)

Dynamic Burn Window Scheduling

The cron runs every 15 min, but glm_quota.py --check gates execution to a burn window of 5-50 min before the 5h quota reset. This ensures nightshift burns remaining budget right before expiry.

How it works:

1. Each cron invocation runs glm_quota.py --check
2. Script reads reset_utc from the quota API
3. Calculates minutes until reset
4. SKIP if: quota >=99% (fully consumed), <5 min until reset (too late), >50 min until reset (too early)
5. RUN if: within 5-50 min window AND quota <99%
6. Adapts task count hint: 3 tasks (<50%), 2 tasks (<75%), 1 task (<95%), scrape remaining (95%+)

The 5h window resets multiple times per day. The reset time drifts, so fixed cron times don't work — the dynamic approach reads reset_utc from the API each invocation.

Configuration (~/.nightshift/config.yaml)

• exclude_repos, min_size_kb, max_repos_to_consider
• public_only (default: true) — only open PRs on public repos. Set to false to include private repos.
• max_inactive_days (default: 30) — skip repos with no pushes in the last X days. Uses the pushed_at field from GitHub API. Set to 0 to disable.
• tasks_per_run, max_prs_per_repo, max_review_iterations
• enabled_categories, enabled_tasks, disabled_tasks
• max_cost_tier, budget_reserve_percent

References (for code-review task)

File	Purpose
solid-checklist.md	SOLID smells + refactor heuristics
security-checklist.md	XSS, injection, auth, race conditions, crypto
code-quality-checklist.md	Error handling, performance, boundary conditions
removal-plan.md	Deletion candidate template
react-patterns.md	React anti-patterns: useEffect, images, hooks (Ralph loop inspired)
opinionated-patterns.md	Language-agnostic best practices for non-React repos

New Ralph-Loop-Inspired Tasks

Inspired by @humanlayer_dev's Ralph loop pattern: pair each task with a best-practice reference doc, then make one targeted fix per run.

• react-effect-cleanup — find and fix useEffect anti-patterns (derived state in effects, stale closures, missing deps). Paired with react-patterns.md. Skips non-JS/TS repos.
• react-image-fix — fix improper Image usage (missing width/height, no alt text, no blur placeholders). Paired with react-patterns.md. Skips non-JS/TS repos.
• lint-doctor-fix — run the project's built-in lint/doctor tools (eslint, ruff, clippy, react-doctor, biome, golangci-lint) and auto-fix what they detect. Language-agnostic. 24h cooldown.
• best-practice-fix — generic opinionated fix. Loads react-patterns.md for JS/TS repos, opinionated-patterns.md for others. Fixes one pattern per run.

Tasks that have a reference field also include reference_content in the task JSON output — the cron agent injects this into plan/implement prompts so the subagent has the best-practice guidance inline.

Pitfalls

• rm -rf blocked. Use shutil.rmtree() via execute_code.
• No formatters (ruff format, biome format, prettier) — only lint fixes.
• No new deps — revert package.json/lockfile changes.
• Most repos are clean — analysis tasks more valuable than lint-only.
• Safe category tasks are expensive (500k-1M tokens) — use sparingly.
• Emergency tasks have 4-week cooldown for a reason.
• gh pr create requires --head BRANCH_NAME — the main agent runs from a different directory than the subagent that created the branch.
• Subagents may already commit changes before the main agent checks git status --porcelain — check git log main..HEAD --oneline instead to verify commits exist.
• GLM 5.1 sometimes produces garbled/non-JSON output in the plan phase (especially for plan_implement_review). If plan output isn't valid JSON, fall back to manual analysis: use compiler/linting tools directly (cargo check/clippy for Rust, ruff/pylint for Python, grep for dead code patterns like commented-out blocks, #[allow(dead_code)], unused imports, unreachable code). Then proceed to the check phase or report "no changes" if the codebase is clean.
• nightshift.py hangs on clone operations — running python3 nightshift.py (even --dry-run) times out after 60-120s. Workaround: run --list-tasks separately (works), then manually select repos using gh repo list, check sizes with gh repo view --json diskUsage, git clone each repo yourself, and dispatch tasks via delegate_task. Read ~/.nightshift/state.json to check recent runs and avoid cooldown violations.
• Large single-file repos kill delegate_task — repos with one large source file (2000+ lines) cause subagents to burn all iterations re-reading the same file. The veyoff delegate_task spent 308s and 250k tokens reading a 2300-line C++ file repeatedly without making edits. Workaround: for repos with few files, do the work directly (read_file, patch) instead of delegating.
• Task-repo compatibility matters — lint-fix on a Windows-only C++ project can't work on Linux (no compiler, no clang-tidy). Always check the tech stack before assigning tasks: C++/Windows → analysis/readme tasks only; Go/Rust/Python/JS → all tasks work. Check CMakeLists.txt, go.mod, package.json, Cargo.toml before choosing.
• Burn window < 20 min → skip medium/high tasks — delegate_task timeouts (5 min) plus review loops mean a 16-min window only fits 1 low-cost manual task. Start with the cheapest task to guarantee at least one result.