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 pjt222/agent-almanac --skill conduct-retrospectivegit clone --depth 1 https://github.com/pjt222/agent-almanacWrote 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/pjt222/agent-almanac/conduct-retrospective)<a href="https://agentmods.dev/skills/pjt222/agent-almanac/conduct-retrospective"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/conduct-retrospective/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/pjt222/agent-almanac/conduct-retrospective"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/conduct-retrospective.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00083 | $0.01816 |
| Opus 5.5 | $0.00033 | $0.00726 |
| Sonnet 5.5 | $0.00017 | $0.00363 |
| Haiku 4.5 | $0.00008 | $0.00182 |
Grade A, and why
conduct-retrospective 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conduct a Retrospective
Facilitate a structured retrospective that reviews recent project execution, identifies what worked and what didn't, and produces actionable improvement items that feed back into project processes. This skill transforms raw project data into evidence-backed learnings with specific actions, owners, and due dates.
When to Use
- End of a sprint (sprint retrospective)
- End of a project phase or milestone
- After a significant incident, failure, or success
- Quarterly review of ongoing project processes
- Before starting a similar project (lessons learned review)
Inputs
- Required: Period under review (sprint number, date range, or milestone)
- Optional: Status reports from the review period
- Optional: Sprint velocity and completion data
- Optional: Previous retrospective actions (to check closure)
- Optional: Team feedback or survey results
Procedure
Step 1: Gather Retrospective Data
Read available artifacts from the review period:
- STATUS-REPORT-*.md files for the period
- SPRINT-PLAN.md for planned vs actual
- BACKLOG.md for item flow and cycle times
- Previous RETRO-*.md for open action items
Extract key facts:
- Items planned vs completed
- Velocity trend
- Blockers encountered and resolution time
- Unplanned work that entered the sprint
- Open action items from previous retrospectives
Got: Data summary with quantitative metrics (velocity, completion %, blocker count).
If fail: If no artifacts exist, base the retrospective on qualitative observations.
Step 2: Structure "What Went Well"
List 3-5 things that worked well, with evidence:
## What Went Well
| # | Observation | Evidence |
|---|------------|---------|
| 1 | [Specific positive observation] | [Metric, example, or artifact reference] |
| 2 | [Specific positive observation] | [Metric, example, or artifact reference] |
| 3 | [Specific positive observation] | [Metric, example, or artifact reference] |
Focus on practices to continue, not only outcomes. "Daily standups kept blockers visible" is more actionable than "We delivered on time."
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 · 205 lines · 83 tokens per session scan A 7f502b438c90
conduct-retrospective is a skill published in the GitHub repository pjt222/agent-almanac (42 stars, last pushed 3d ago), licensed MIT. It adds 83 tokens to every session and 1,816 once invoked, about $0.0003 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 2026-09-03.
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