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/boshu2/agentops/plannpx skills add boshu2/agentops --skill plangit clone --depth 1 https://github.com/boshu2/agentopsWhat 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 | $0.00033 | $0.01270 |
| Opus 5 | $0.00016 | $0.00635 |
| Sonnet 5 | $0.00007 | $0.00254 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
plan 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan
Turn the caller's intent into one bounded, testable behavior in the place that already owns the work. Prefer the caller's tracker, if any. When no durable tracker or issue reference is available, use the caller's conversation or supplied text; the runtime snapshots those resolved intent bytes so later contexts can read and hash the same source. Do not make the model restate those facts in a packet.
Workflow
- Resolve the intent source and choose one active behavior. When that source
is not already durable, have the runtime pass its exact bytes to the
validate skill's
scripts/validate.py snapshot-intent --source -, resolved relative to wherever that skill package is installed (a repo checkout:skills/validate/scripts/validate.py; an installed skill package:.agents/skills/validate/scripts/validate.py), and use the returnedintent_reffor later phases. - Route the work by type (see Ground-truth routing) and name its ground truth first. Then inspect only enough real context to make paths, interfaces, and evidence concrete: hydrate only the context sources this decision needs and carry their citations forward. Existing research and specialist skills are advisory inputs, never a merged context store.
- Ensure the source contains acceptance examples, important non-goals, and the allowed write scope. Use lightweight prose or Given/When/Then only where it removes ambiguity; do not require both normal and edge ceremony for every change.
- Name the first useful acceptance check.
- If authorized and the source is writable, update that bead or issue in place. Otherwise return a concise proposed amendment to the caller.
Planning produces no AgentOps packet. A durable caller-owned source stays in place; the runtime carries its reference and the digest of its exact resolved bytes to detect later acceptance drift. Only when no durable source exists does the runtime store those bytes under their digest as a content-addressed snapshot. That fallback is derived automatically and is not another model-authored planning artifact.
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 · 111 lines · 33 tokens per session scan A 8582f5e21215
plan is a skill published in the GitHub repository boshu2/agentops (431 stars, last pushed 4d ago), licensed Apache-2.0. It adds 33 tokens to every session and 1,270 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
brainstorm
Explore vague or ambitious ideas into a right-sized requirements-only plan. Use when the user wants to brainstorm, think through scope, decide what to build, or needs collaborative product framing before planning, not for a decisive verdict on whether to adopt or switch to a specific external technology, library, or…
autolearn
Compound a solved problem into a durable in-repo learning doc. Use when a verified non-trivial fix lands, the user says "compound this", "document this fix", or "remember this". This is the automatic-capture entry point; for an explicitly requested one-off write-up, use compound.
doc-review
Use when the user asks to review or critique a prose planning document — a plan, spec, PRD, requirements doc, or design doc.
audit-project
Run an iterative multi-agent code audit until critical and high findings are resolved. Use when the user says "audit my code", "find all the bugs", "deep code audit", "iterative review", or "review until clean".
commit-push-pr
Use when asked to ship/open a PR, or for PR-description-only flows like writing, rewriting, or describing a PR body.
doubt-driven
Doubt-driven adversarial review. Use when correctness matters more than speed, the code is unfamiliar, stakes are high, a claim can't be checked by the type system or compiler, or verifying now is cheaper than debugging later.