Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/shivaedev/pardes/audit-workstream-runnpx skills add ShivaeDev/pardes --skill audit-workstream-rungit clone --depth 1 https://github.com/ShivaeDev/pardesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shivaedev/pardes/audit-workstream-run)<a href="https://agentmods.dev/skills/shivaedev/pardes/audit-workstream-run"><img src="https://agentmods.dev/badge/skills/shivaedev/pardes/audit-workstream-run.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00056 | $0.00437 |
| Opus 5 | $0.00028 | $0.00218 |
| Sonnet 5 | $0.00011 | $0.00087 |
| Haiku 4.5 | $0.00006 | $0.00044 |
Grade A, and why
audit-workstream-run scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Audit Workstream Run
Evaluate another Codex session without loading its full transcript into the manager context. Use the result to improve reusable workflow skills.
Workflow
- Read only lightweight metadata first:
${CODEX_HOME:-$HOME/.codex}/session_index.jsonl- rollout filenames under
${CODEX_HOME:-$HOME/.codex}/sessions/ - the first
session_metaline when needed
- Identify the relevant user-started parent rollout and direct descendants by
session ID and
forked_from_id. - Spawn a fresh read-only explorer to inspect the parent transcript and the minimum descendant transcript needed for a workflow audit.
- Ask the explorer to omit implementation detail unless it explains a process failure. Require verified evidence and a separate inference section.
- When the audit becomes the blocker, wait on the explorer with a
900000ms timeout. Treat an ordinary timeout as still working and wait again without short polling. - Present a compact audit and proposed skill improvements. Do not edit skills until the user approves the revision batch.
Explorer Brief
Request:
- chronological workflow timeline
- pipeline transitions and unnecessary branching
- explorer, worker, and verifier delegation behavior
- worker wait duration and repeated polling
- worktree helper use and sandbox routing
- PR-cycle publication, browser handoff, and monitor behavior
- compaction or interrupted-wait recovery
- actionable skill improvements ordered by impact
- source pointers using session IDs and timestamps
Rules
- Keep raw rollout JSONL out of the manager context.
- Prefer one focused audit explorer over manager-side transcript reading.
- Read descendant metadata and final reports first; expand only when needed.
- Separate observed facts from hypotheses.
- Evaluate the workflow, not the implementation.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 50 lines · 56 tokens per session scan A 69832fd6c90a
audit-workstream-run is a skill published in the GitHub repository ShivaeDev/pardes (5 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 437 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…