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/liwala/sheal/retronpx skills add liwala/sheal --skill retrogit clone --depth 1 https://github.com/liwala/shealWrote 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/liwala/sheal/retro)<a href="https://agentmods.dev/skills/liwala/sheal/retro"><img src="https://agentmods.dev/badge/skills/liwala/sheal/retro.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 | $0.00039 | $0.00835 |
| Opus 5 | $0.00019 | $0.00417 |
| Sonnet 5 | $0.00008 | $0.00167 |
| Haiku 4.5 | $0.00004 | $0.00084 |
Grade A, and why
retro 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 5d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Retrospective Skill
You are performing a deep retrospective analysis of a completed AI coding session. This is the self-healing loop: you're reviewing what happened and generating actionable rules to improve future sessions.
Step 1: Gather Data
Run the static analysis first. If a checkpoint ID was provided as an argument, use it. Otherwise, use the latest checkpoint.
sheal retro --format json [-c CHECKPOINT_ID]
Also load the session details to see the Entire.io summary:
sheal sessions --format json [-c CHECKPOINT_ID]
Step 2: Deep Analysis
With the static analysis and session data loaded, analyze the session deeply. Consider:
Failure Patterns
- For each failure loop or Bash failure: why did it happen? Was it a wrong approach, missing context, environment issue, or normal iteration?
- Could a pre-check have prevented it?
- Was the agent stuck or making progress?
Effort Quality
- Was the file churn productive iteration or wasted effort?
- Were the right tools used? (e.g., using Bash when Read/Grep would have been better)
- Was research done before implementation, or was the approach trial-and-error?
Missing Context
- What information was missing at the start that caused problems later?
- What should have been in CLAUDE.md, .cursorrules, or project documentation?
- Were there assumptions that turned out wrong?
Workflow Improvements
- What would make the next session smoother?
- Are there recurring patterns that should become rules?
- Should any health checks be added to
sheal check?
Step 3: Generate Output
Present your analysis as a structured retrospective report:
Report Format
## Session Retrospective: [checkpoint-id]
### Summary
[1-2 sentence summary of what happened and how it went]
### Health Score: [X/100]
[Explain why this score, what went well, what didn't]
### Key Findings
#### What Went Well
- [thing 1]
- [thing 2]
#### What Could Be Improved
- [issue 1]: [why it happened] → [what to do differently]
- [issue 2]: [why it happened] → [what to do differently]
### Suggested Rules
[List specific rules that should be added to agent config files]
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.
- 5d ago First seen · 105 lines · 39 tokens per session scan A 14c48c17e087
retro is a skill published in the GitHub repository liwala/sheal (86 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 835 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
ads-linkedin
Audit LinkedIn Ads measurement, Insight Tag and conversions, professional audiences, lead generation, ABM, creative, bidding, budgets, pacing, automation, and policy. Use for LinkedIn Ads, Campaign Manager, Insight Tag, Lead Gen Forms, Thought Leader Ads, ABM campaigns, or B2B paid media.
ads-launch
Draft or explicitly apply a paid-ad campaign launch through Claude Ads capability-gated adapters. Use for campaign creation, launch plans, publishing ads, activating campaigns, uploading creative, or requests to push a campaign live.
bun-test-mocking
Use for mock functions in Bun tests, spyOn, mock.module, implementations, and test doubles.
api-response-optimization
Optimizes API performance through payload reduction, caching strategies, and compression techniques. Use when improving API response times, reducing bandwidth usage, or implementing efficient caching.
plan-create
Create structured implementation plans for autonomous TDD development. Use for new features, multi-file changes, or anything requiring multiple steps or tests. Triggers on aspirational openers ("let's build", "let's start building", "I want to make", "I want an app that", "help me build"), capability lists ("users…
blog-multilingual
One-command multilingual blog creation. Writes a blog post, translates it into user-specified languages, applies cultural adaptation, and emits hreflang tags, sitemap entries, and a CMS-ready language map. The complete write-to-publish pipeline for international content. Orchestrates blog-write, blog-translate…