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/tta-lab/ttal-cli/sp-planningnpx skills add tta-lab/ttal-cli --skill sp-planninggit clone --depth 1 https://github.com/tta-lab/ttal-cliWrote 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/tta-lab/ttal-cli/sp-planning)<a href="https://agentmods.dev/skills/tta-lab/ttal-cli/sp-planning"><img src="https://agentmods.dev/badge/skills/tta-lab/ttal-cli/sp-planning.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 | $0.00023 | $0.00561 |
| Opus 5 | $0.00012 | $0.00280 |
| Sonnet 5 | $0.00005 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
sp-planning 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Planning
Write a plan that a skilled developer can execute without rediscovering the design. FlickNote is the single source of truth.
1. Explore Reality
Before planning:
- Read the relevant code, tests, docs, and repository rules.
- Search prior work with
flicknote find <keywords>. - Map existing constraints, interfaces, dependencies, and failure modes.
- Confirm the target repository.
Do not design from filenames or assumptions.
2. Confirm the Approach
Before writing the detailed plan, tell the user:
- What exists now
- The proposed approach and why
- The main trade-off or risk
Stop for explicit alignment if the direction has not already been approved.
3. Define the Plan
Include:
- Goal and anti-goals
- Exit criteria expressed as observable behavior
- Exact scope and files
- Ordered implementation stages
- Test strategy and exact verification commands
- Risks, dependencies, and rollout concerns
For behavior changes and bug fixes, plan a TDD sequence:
- Add a focused test.
- Run it and confirm the expected failure.
- Implement the minimum change.
- Run focused and broader tests.
- Refactor only while green.
Mechanical deletions, generated files, and configuration-only work do not need invented tests; state the appropriate verification instead.
4. Keep Scope Executable
Split the plan into separate deliverable phases when:
- Multiple repos can ship independently
- A stage has a separate rollback boundary
- The plan is too large to review or verify as one change
Record dependencies between phases in the plan. Do not create tasks automatically.
5. Persist to FlickNote
Create or update one note in the orientation project:
flicknote add --project orientation flicknote detail --tree flicknote modify
Use this structure:
Plan:
Goal
Anti-goals
Current state
Approach
Implementation stages
Test strategy
Exit criteria
Risks and dependencies
Each stage must name the files, behavior change, tests, verification, and commit boundary when useful.
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 · 95 lines · 23 tokens per session scan A 34c42a87cce3
sp-planning is a skill published in the GitHub repository tta-lab/ttal-cli (23 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 561 once invoked, about $0.0001 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…