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.
git clone --depth 1 https://github.com/naimkatiman/continuous-improvementWrote 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/commands/naimkatiman/continuous-improvement/proceed-with-the-recommendation)<a href="https://agentmods.dev/commands/naimkatiman/continuous-improvement/proceed-with-the-recommendation"><img src="https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/proceed-with-the-recommendation/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/commands/naimkatiman/continuous-improvement/proceed-with-the-recommendation"><img src="https://agentmods.dev/badge/commands/naimkatiman/continuous-improvement/proceed-with-the-recommendation.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00048 | $0.01020 |
| Opus 5 | $0.00024 | $0.00510 |
| Sonnet 5 | $0.00010 | $0.00204 |
| Haiku 4.5 | $0.00005 | $0.00102 |
Grade A, and why
proceed-with-the-recommendation scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -L https://raw.githubusercontent.com/naimkatiman/continuous-improvement/main/skills/proceed-with-the-recommendation.md \ How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/proceed-with-the-recommendation
Walk an agent-generated recommendation list top-to-bottom under the continuous-improvement 7 Laws. Each item is routed to the best specialist skill when available, falls back to concrete inline behavior when it is not.
Invoke immediately after the agent has offered a numbered list of recommendations, next steps, or suggested actions.
Trigger phrases
/proceed-with-the-recommendation- "proceed with your recommendation"
- "do all of it"
- "go ahead with the plan"
- "execute the recommendations"
- "yes do it" / "all of them"
Fast-path: --once mode
For a single-item, safe-tagged confirmation, append --once to skip P-MAG, Plan, Reflect, and the three-section close. Runs Phase 1 + Phase 3 + Phase 4 only and ends with one line: Done: <item>. Verified: <check + output>.
Trigger phrases:
/proceed-with-the-recommendation --once/proceed --once- "proceed once with that" / "just do that one"
- Any standard trigger phrase plus the literal
--oncesuffix
Hard preconditions (the skill refuses --once and falls back to the full flow if any fail):
- Recommendation list has exactly one item
- Item is tagged
safe(notcaution, notneeds-approval) - Item touches ≤3 files, ≤150 LOC, and none of
.github/,bin/,lib/,src/,third-party/ - Item is not destructive (no deploy, force-push, DB drop, secret rotation)
Full behavior is defined in skills/proceed-with-the-recommendation.md under § "Fast-Path: --once Mode".
What happens
- Pre-flight (Law 1) — restate the recommendation list in original order, tag each
safe/caution/needs-approval - Plan (Law 2) — inline restatement if ≤3 items, call
superpowers:writing-plansif larger - Execute (Law 3) — route each item to the preferred skill; apply inline fallback if the skill is not installed
- Verify (Law 4) — smallest check per item, non-transitive (no later item retroactively verifies an earlier one)
- Iterate (Law 6) — one change → verify → next; never carry a failure forward
- Reflect (Laws 5 + 7) — end-of-run Reflection block appended to
observations.jsonl - Close — three-section user-facing block: What has been done → What is next → Recommendation (tiered tables + one decisive "My recommendation" paragraph + a binary "Want me to: A or B?" closer)
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.
- 9d ago First seen · 83 lines · 48 tokens per session scan A 403ce9c5113a
proceed-with-the-recommendation is a command published in the GitHub repository naimkatiman/continuous-improvement (7 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 1,020 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
ap-swarm
Run a MultiAgentRuntime swarm in file mode — local journal at .runtime/conversation.md, no network.
reflect-meta
Show meta-learning statistics and pattern performance.
reflect-promote
Promote learnings from skills to global CLAUDE.md.
reflect-stats
Show cross-skill learning statistics.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.