Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/computerlovetech/agrnpx agentmods add skills/computerlovetech/agr/skill-debriefWrote 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/computerlovetech/agr/skill-debrief)<a href="https://agentmods.dev/skills/computerlovetech/agr/skill-debrief"><img src="https://agentmods.dev/badge/skills/computerlovetech/agr/skill-debrief/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/computerlovetech/agr/skill-debrief"><img src="https://agentmods.dev/badge/skills/computerlovetech/agr/skill-debrief.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
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 →
- low Excessive Agency · line 249 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00219 | $0.02264 |
| Opus 5.5 | $0.00088 | $0.00906 |
| Sonnet 5.5 | $0.00044 | $0.00453 |
| Haiku 4.5 | $0.00022 | $0.00226 |
Grade A, and why
skill-debrief 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 1mo 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Debrief
Capture lessons from a session into the skill that drove it. The default shape is listen → propose → align → apply → re-install.
When to use
Trigger when the user wants to debrief an existing SKILL.md based on what happened in the session. Examples:
- "debrief the X skill" / "let's debrief X"
- "retrospective on X" / "feedback on X"
- "improve the X skill" / "let's update X based on what we learned"
- "X skill should also handle …"
- "X didn't trigger when it should have"
Do NOT use this skill for:
- Greenfield skill authoring. Use
agr initto scaffold a SKILL.md and defer the body content to the user — or to a dedicated authoring skill such asanthropics/skills/skill-creator(agr add anthropics/skills/skill-creator). - Installing / syncing / removing skills. That's plain
agrCLI work (agr add,agr sync,agr upgrade,agr remove).
Step 1: Identify the skill
Ask which skill is being improved if it isn't obvious from context. Then locate the source:
agr list # see installed deps and short names
ls skills/ # in-repo source if present
cat agr.toml # see whether the dep is local-path or remote
Two cases — they have different update paths:
| Case | Source location | Update path |
|---|---|---|
In-repo ({path = "./skills/<name>", type = "skill"} in agr.toml) |
skills/<name>/ |
Edit source → commit → agr upgrade <name> |
Remote ({handle = "user/repo/<name>", …}) |
Upstream GitHub repo | Cannot edit directly — see Step 5 |
If the skill isn't installed at all but the user wants to improve it, ask whether to add it first (and which case applies).
Step 2: Receive feedback
Listen. The user invoked this skill because they have something to say — let them say it. Do not interrogate. Do not run a checklist of questions at them. Take in whatever they offer, in whatever shape they offer it.
Only ask a clarifying question if you genuinely cannot proceed without one (e.g. the user named a skill that doesn't exist, or two skills share the name and you need to disambiguate). Even then, ask the minimum.
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.
- 1mo ago First seen · 257 lines · 219 tokens per session scan A 57be7613d0bd
skill-debrief is a skill published in the GitHub repository computerlovetech/agr (452 stars, last pushed 2mo ago), licensed MIT. It adds 219 tokens to every session and 2,264 once invoked, about $0.0009 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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