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/tessaryai/plugins/process-backlognpx skills add tessaryai/plugins --skill process-backloggit clone --depth 1 https://github.com/tessaryai/pluginsWhat 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 | $0.00065 | $0.00750 |
| Opus 5 | $0.00032 | $0.00375 |
| Sonnet 5 | $0.00013 | $0.00150 |
| Haiku 4.5 | $0.00006 | $0.00075 |
Grade A, and why
process-backlog scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
process-backlog
Internal crew primitive — dispatched by
/crew:run. You are running because the orchestrator selected this as one step of a larger workflow; carry out the work below. This skill is not meant to be invoked on its own — user requests go to/crew:run.
You recover work that stalled (rate limits, timeouts, missed events). You find stuck items and advance a bounded number of them, then stop. Your ceiling is a review-ready PR — never merge.
0. Load config and mode
python3 "${CLAUDE_PLUGIN_ROOT}/lib/load_config.py"
Use labels.{bug,task,triaged,agent_pr}, ledger.dir, and orchestrator.max_items (the
per-run cap; default 5 if unset). Split the budget across the backlogs below.
Then read ${CLAUDE_PLUGIN_ROOT}/reference/work-model.md and resolve the mode before any
gh call. In github mode use steps 1–2; in local mode use step 1L.
1. (GitHub) Find triaged issues with no PR
gh issue list --state open --label "<labels.triaged>" --json number,title,labels
Keep those that also carry labels.bug or labels.task. For each, check there is no
linked PR (search PRs/branches for Fixes #N / crew/issue-N). These need implementation.
2. (GitHub) Find crew PRs with unaddressed reviews
gh pr list --state open --label "<labels.agent_pr>" --json number,reviewDecision,title
Keep those whose latest review state is CHANGES_REQUESTED (or that have unresolved review
comments). These need a review response.
1L. (Local) Scan the ledger for stalled tasks
Read every <ledger.dir>/*/task.md and group by status (skip terminal done and
needs_human):
new→ needs triage (triage-bug/triage-taskbykind).triaged→ needs implementation (implement-issue).changes_requested→ needs a review response (respond-to-review).implemented→ needs review (review-pr).
3. Advance, up to the cap
Process at most orchestrator.max_items items total, one at a time, dispatching the
primitive each item's state calls for (above).
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.
- 2d ago First seen · 73 lines · 65 tokens per session scan A 0c03cd84babc
process-backlog is a skill published in the GitHub repository tessaryai/plugins (3 stars, last pushed 14d ago), licensed MIT. It adds 65 tokens to every session and 750 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.
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