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 skills add sandover/ergo --skill ergo-backlog-planninggit clone --depth 1 https://github.com/sandover/ergoWrote 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/sandover/ergo/ergo-backlog-planning)<a href="https://agentmods.dev/skills/sandover/ergo/ergo-backlog-planning"><img src="https://agentmods.dev/badge/skills/sandover/ergo/ergo-backlog-planning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sandover/ergo/ergo-backlog-planning"><img src="https://agentmods.dev/badge/skills/sandover/ergo/ergo-backlog-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 52 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 40 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 86 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 5.1 | $0.00042 | $0.00372 |
| Opus 5.5 | $0.00017 | $0.00149 |
| Sonnet 5.5 | $0.00008 | $0.00074 |
| Haiku 4.5 | $0.00004 | $0.00037 |
Grade A, and why
ergo-backlog-planning 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.
What it actually says
Ergo Backlog Planning
Use Ergo to write tasks (work tickets) to be implemented by you or another agent. Each task should include a clear unambiguous goal, essential context and rationale, links to any necessary resources or background, a crisp definition of done (often a list), and, where applicable, suggestions for how to validate the work.
If you do not know the ergo CLI already, read ergo --help and ergo quickstart. Use ergo where to confirm the backlog's location before writing tasks.
- Use reasonable assumptions for routine details. Ask the user when available context cannot resolve a choice that materially changes scope, risk, or what counts as done.
- Split work where an agent can deliver a useful outcome; keep small related steps together.
- Use epics to help organize related tasks.
- Tasks should make sense on their own, but the epic body may provide larger context.
- Assume the implementer is as intelligent as you are, but they lack your conversational context. You don't need to be overly verbose or prescriptive; let the implementer implement.
- Sometimes only a person can judge whether a task is done. If so, say what the human will review and what decision is needed.
- Add a dependency when one task needs another task's result. Leave independent tasks free to run in parallel.
- Keep validation as cheap as possible, checking only what is necessary to establish that the task is done.
- Reuse check results that still apply to the work. Add a combined check only when individual task checks cannot establish the shared outcome.
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 Changed · -50 lines · -9 tokens per session dd946b17a6ac
- 25d ago Changed · -45 lines · -22 tokens per session 9dc5b7a3e053
- 1mo ago Changed · -13 lines 25c3033168dd
- 1mo ago First seen · 130 lines · 73 tokens per session scan A d0ea1fd8a41c
ergo-backlog-planning is a skill published in the GitHub repository sandover/ergo (44 stars, last pushed 3d ago), licensed MIT. It adds 42 tokens to every session and 372 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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