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/greatmark/fable-field-guide-skills/implementation-plannpx skills add GreatMark/fable-field-guide-skills --skill implementation-plangit clone --depth 1 https://github.com/GreatMark/fable-field-guide-skillsWrote 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/greatmark/fable-field-guide-skills/implementation-plan)<a href="https://agentmods.dev/skills/greatmark/fable-field-guide-skills/implementation-plan"><img src="https://agentmods.dev/badge/skills/greatmark/fable-field-guide-skills/implementation-plan.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 | $0.00164 | $0.01578 |
| Opus 5 | $0.00082 | $0.00789 |
| Sonnet 5 | $0.00033 | $0.00316 |
| Haiku 4.5 | $0.00016 | $0.00158 |
Grade A, and why
implementation-plan 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Plan
A plan's job is to surface the things the user might actually need to alter, before changing them becomes expensive. Order it by likelihood of change, not by execution order.
Workflow
- Check the escape hatch. If the change is single-file, touches no interface, data, or UX, and has only one sensible way to do it, say so in one sentence and start — no plan. If a reviewed plan for this task already exists, do not rewrite it: confirm it still stands, then start implementing with implementation-notes.
- Gather the inputs. Collect what this plan is based on: the brainstorm,
spec, interview decisions (from earlier in the conversation, or from
interview-decisions.mdin the project root if the interview ran in a previous session), an existingimplementation-plan.mdwhen continuing earlier planning, the porting checklist from an earlier reference hunt (porting-checklist.md, if present), any chosen prototype under./design-directions/, plus a read of the code that will be touched. If the chat contradictsinterview-decisions.md, the latest chat statement wins — but promote the conflict to a pinned decision entry ("changed from X per your latest comment — confirm?"). If key inputs are missing: when the missing answers would overturn more than one top decision, run interview-me first; otherwise record explicit assumptions marked "(assumed — not confirmed)" and pin them at the top of the decision section regardless of sort order. - Sort by likelihood of change, not chronology. Lead with the decisions the user is most likely to tweak — data model and schema changes, new type interfaces and API shapes, anything user-facing (examples; the authoritative sort key is in Rules). Bury the mechanical refactoring at the bottom — the user trusts the agent on that part.
- Make the top decisions reviewable. For each high-change-risk decision: the chosen option, 1-2 alternatives considered, and what changing it later would cost.
- Mark the improvisation zones. Flag the areas where unknowns are likely to surface mid-implementation, and state the fallback posture for each (e.g. "pick the conservative option and log it").
- End with an explicit go/no-go. Present the plan in chat — decision
list first — and ask the user to approve, adjust, or reject. If review
spans sessions or more than one round, persist the current draft as
implementation-plan.mdmarkedstatus: draft(apply the step 6 exclude when first writing it); flip it toapprovedon approval. On a vague pass ("looks fine, go ahead"), treat only the decisions actually discussed as approved: restate each undiscussed high-risk decision in one line, then persist. - On approval, persist and hand off. Write the approved plan to
implementation-plan.mdin the project root (or flip the draft's status), in the language of the conversation, using the skeleton below. If the file already exists from a different task, never overwrite it silently: append a new plan section, or rename the old file toimplementation-plan-<date>.mdand say so. Do not commit it unless the user asks. If this is a git repo, exclude it locally:n=implementation-plan.md; p="$(git rev-parse --git-path info/exclude 2>/dev/null)" && { mkdir -p "$(dirname "$p")" && { grep -qxF "$n" "$p" || echo "$n" >> "$p"; }; } 2>/dev/null || echo "note: could not exclude $n — leaving it untracked, never staging it"Then suggest starting implementation in a fresh session withimplementation-plan.md(and any prototypes) passed in as artifacts. Either way, once implementation kicks off — especially in a new session — keep notes per the implementation-notes skill, withimplementation-plan.mdas the Context input ofimplementation-notes.md.
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 · 126 lines · 164 tokens per session scan A 4e9ba9ac2774
implementation-plan is a skill published in the GitHub repository GreatMark/fable-field-guide-skills (9 stars, last pushed 28d ago), licensed MIT. It adds 164 tokens to every session and 1,578 once invoked, about $0.0008 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 2026-08-31.
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