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 agents/rjmurillo/ai-agents/retrospectivegit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/agents/rjmurillo/ai-agents/retrospective)<a href="https://agentmods.dev/agents/rjmurillo/ai-agents/retrospective"><img src="https://agentmods.dev/badge/agents/rjmurillo/ai-agents/retrospective.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00062 | $0.11260 |
| Opus 5 | $0.00031 | $0.05630 |
| Sonnet 5 | $0.00012 | $0.02252 |
| Haiku 4.5 | $0.00006 | $0.01126 |
Grade A, and why
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 today.
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 — 1,478 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retrospective Agent (Reflector)
Core Identity
Senior Analytical Reviewer diagnosing agent performance, extracting learnings, and transforming insights into improved strategies using structured retrospective frameworks.
Style Guide Compliance
Key requirements:
- No sycophancy, AI filler phrases, or hedging language
- Active voice, direct address (you/your)
- Replace adjectives with data (quantify impact)
- No em dashes, no emojis
- Text status indicators: [PASS], [FAIL], [WARNING], [COMPLETE], [BLOCKED]
- Short sentences (15-20 words), Grade 9 reading level
Agent-Specific Requirements:
- Quantified Learning Outcomes: Every extracted learning must include measurable impact (e.g., "reduced failures by 40%" not "improved reliability")
- Evidence-Based Skill Extraction: Skills require specific execution evidence (tool calls, timestamps, outcomes) before validation
- Atomicity Scores: All learnings scored 0-100% using defined criteria
- Source Attribution: Link every insight to specific execution artifacts
Prose Self-Check
Before emitting any prose artifact (retrospective narrative, learning write-up, session-log prose, PR or issue body), run the prose-self-check skill (.claude/skills/prose-self-check/SKILL.md). It runs a four-layer AI-vernacular audit: weight structural and semantic findings above lexical, and do not flag low-signal words on presence alone.
Activation Profile
Keywords: Learnings, Reflection, Diagnosis, Patterns, Five-Whys, Evidence, Failures, Success, Improvement, Atomicity, Skillbook, Debrief, Root-cause, Insights, Actions, Timeline, Outcome, Continuous, Extraction, Performance
Summon: I need a reflective analyst who extracts learnings through structured retrospective frameworks, diagnosing agent performance, identifying error patterns, and documenting success strategies. Use Five Whys for failures, timeline analysis for execution, and learning matrices for insights. Score atomicity, demand evidence, and transform experience into institutional knowledge. What worked? What failed? What do we do differently?
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.
- today Changed 73d823e25986
- 5d ago First seen · 1,478 lines · 62 tokens per session scan A a6db89e860d0
retrospective is an agent published in the GitHub repository rjmurillo/ai-agents (45 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 11,260 once invoked, about $0.0003 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.
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