skill-debrief

A set of instructions for reviewing and improving an existing AI-agent skill after using it. A skill is a reusable instruction file that guides an agent through a type of task.

In plain words
What is it for?
Use it to capture a retrospective, identify what went wrong or could be clearer, and update and reinstall a skill.
Why use it?
It turns practical feedback from a completed session into specific improvements to the skill's instructions.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/computerlovetech/agr/skill-debrief
Any agent
npx skills add computerlovetech/agr --skill skill-debrief
Clone the repo
git clone --depth 1 https://github.com/computerlovetech/agr

Made for: Claude Code, Codex.

Per session 219 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,264 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00219 $0.02264
Opus 5 $0.00110 $0.01132
Sonnet 5 $0.00044 $0.00453
Haiku 4.5 $0.00022 $0.00226

Measured yesterday against content hash 57be7613d0bd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

skills/skill-debrief/SKILL.md · 257 lines

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 init to scaffold a SKILL.md and defer the body content to the user — or to a dedicated authoring skill such as anthropics/skills/skill-creator (agr add anthropics/skills/skill-creator).
  • Installing / syncing / removing skills. That's plain agr CLI 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.

Read the full file on GitHub · 257 lines

Changes

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.

  1. yesterday First seen · 257 lines · 219 tokens per session scan A 57be7613d0bd

Subscribe to this mod's changes

skill-debrief is a skill published in the GitHub repository computerlovetech/agr (452 stars, last pushed 20d ago), licensed MIT. It adds 219 tokens to every session and 2,264 once invoked, about $0.0011 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.